/jhdZddlmZddlmZmZddlmZddlm Z ddl Z ddl Z ddl Z ddlmcmZddlmZddlmZdd lmZdd lmZmZmZdd lmZmZdd lm Z m!Z!d dl"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+d dl,m-Z-GddeZ.Gdde.Z/Gdde/Z0Gdde/Z1Gdde/Z2Gdde2Z3Gdde0e2Z4Gdde3Z5dS) a Generate predictions using the Segment Anything Model (SAM). SAM is an advanced image segmentation model offering features like promptable segmentation and zero-shot performance. This module contains the implementation of the prediction logic and auxiliary utilities required to perform segmentation using SAM. It forms an integral part of the Ultralytics framework and is designed for high-performance, real-time image segmentation tasks. ) annotations) OrderedDict defaultdict)deepcopy)AnyN) LetterBox) BasePredictor)Results) DEFAULT_CFGLOGGERops)box_ioumask_iou) select_devicesmart_inference_mode) batch_iteratorbatched_mask_to_boxbuild_all_layer_point_gridscalculate_stability_scoregenerate_crop_boxesis_box_near_crop_edgeremove_small_regionsuncrop_boxes_xyxy uncrop_masks)PromptceZdZdZdZeddfd"fd ZdZdZd#d Z d#d Z d#d Z d$d Z d%dZ d&dZdZdZdZfdZdZdZdZed'd Ze d(d!ZxZS)) PredictoraPredictor class for SAM, enabling real-time image segmentation with promptable capabilities. This class extends BasePredictor and implements the Segment Anything Model (SAM) for advanced image segmentation tasks. It supports various input prompts like points, bounding boxes, and masks for fine-grained control over segmentation results. Attributes: args (SimpleNamespace): Configuration arguments for the predictor. model (torch.nn.Module): The loaded SAM model. device (torch.device): The device (CPU or GPU) on which the model is loaded. im (torch.Tensor): The preprocessed input image. features (torch.Tensor): Extracted image features. prompts (dict[str, Any]): Dictionary to store various types of prompts (e.g., bboxes, points, masks). segment_all (bool): Flag to indicate if full image segmentation should be performed. mean (torch.Tensor): Mean values for image normalization. std (torch.Tensor): Standard deviation values for image normalization. Methods: preprocess: Prepare input images for model inference. pre_transform: Perform initial transformations on the input image. inference: Perform segmentation inference based on input prompts. prompt_inference: Internal function for prompt-based segmentation inference. generate: Generate segmentation masks for an entire image. setup_model: Initialize the SAM model for inference. get_model: Build and return a SAM model. postprocess: Post-process model outputs to generate final results. setup_source: Set up the data source for inference. set_image: Set and preprocess a single image for inference. get_im_features: Extract image features using the SAM image encoder. set_prompts: Set prompts for subsequent inference. reset_image: Reset the current image and its features. remove_small_regions: Remove small disconnected regions and holes from masks. Examples: >>> predictor = Predictor() >>> predictor.setup_model(model_path="sam_model.pt") >>> predictor.set_image("image.jpg") >>> bboxes = [[100, 100, 200, 200]] >>> results = predictor(bboxes=bboxes) N _callbacks dict | Nonec|i}|tdddt|||d|j_d|_d|_i|_d|_ dS)aeInitialize the Predictor with configuration, overrides, and callbacks. Sets up the Predictor object for SAM (Segment Anything Model) and applies any configuration overrides or callbacks provided. Initializes task-specific settings for SAM, such as retina_masks being set to True for optimal results. Args: cfg (dict): Configuration dictionary containing default settings. overrides (dict | None): Dictionary of values to override default configuration. _callbacks (dict | None): Dictionary of callback functions to customize behavior. Nsegmentpredictr)taskmodebatchTF) updatedictsuper__init__args retina_masksimfeaturesprompts segment_allselfcfg overridesr __class__s c/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/models/sam/predict.pyr+zPredictor.__init__Ws|  I99AFFFGGG i444!%    c|j|jSt|tj }|rot j||}|ddddfd}t j|}tj |}| |j }|r||j z |j z }|jjr|n|}|S)avPreprocess the input image for model inference. This method prepares the input image by applying transformations and normalization. It supports both torch.Tensor and list of np.ndarray as input formats. For OpenCV-loaded images, the input is typically BGR and is converted to RGB during preprocessing. Args: im (torch.Tensor | list[np.ndarray]): Input image(s) in BCHW tensor format or a list of HWC NumPy arrays. NumPy arrays are expected to be in BGR order (as returned by OpenCV) and will be converted to RGB. Returns: (torch.Tensor): The preprocessed image tensor, normalized and converted to the appropriate dtype. Examples: >>> predictor = Predictor() >>> image = torch.rand(1, 3, 640, 640) >>> preprocessed_image = predictor.preprocess(image) N.)rr)r. isinstancetorchTensornpstack pre_transform transposeascontiguousarray from_numpytodevicemeanstdmodelfp16halffloat)r3r. not_tensors r7 preprocesszPredictor.preprocessms& 7 7N#B 555  &$,,R0011BC2I((66B%b))B!"%%B UU4;    -ty.DH,B*/ 9RWWYYYrxxzz r8ct|dks Jdt|jddfd|DS)0Perform initial transformations on the input image for preprocessing. This method applies transformations such as resizing to prepare the image for further preprocessing. Currently, batched inference is not supported; hence the list length should be 1. Args: im (list[np.ndarray]): List containing a single image in HWC numpy array format. Returns: (list[np.ndarray]): List containing the transformed image. Raises: AssertionError: If the input list contains more than one image. Examples: >>> predictor = Predictor() >>> image = np.random.rand(480, 640, 3) # Single HWC image >>> transformed = predictor.pre_transform([image]) >>> print(len(transformed)) 1 r6SAM model does not currently support batched inferenceF)autocenterc(g|]}|S)image.0x letterboxs r7 z+Predictor.pre_transform..&///q """///r8lenrimgszr3r.r\s @r7rBzPredictor.pre_transformsO,2ww!|||U|||djuUCCC ////B////r8Fch|jd|}|jd|}|jd|}|jd|}td|||fDr|j|g|Ri|S|||||||S)a=Perform image segmentation inference based on the given input cues, using the currently loaded image. This method leverages SAM's (Segment Anything Model) architecture consisting of image encoder, prompt encoder, and mask decoder for real-time and promptable segmentation tasks. Args: im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W). bboxes (np.ndarray | list | None): Bounding boxes with shape (N, 4), in XYXY format. points (np.ndarray | list | None): Points indicating object locations with shape (N, 2), in pixels. labels (np.ndarray | list | None): Labels for point prompts, shape (N,). 1 = foreground, 0 = background. masks (np.ndarray | None): Low-resolution masks from previous predictions, shape (N, H, W). For SAM H=W=256. multimask_output (bool): Flag to return multiple masks. Helpful for ambiguous prompts. *args (Any): Additional positional arguments. **kwargs (Any): Additional keyword arguments. Returns: pred_masks (torch.Tensor): The output masks in shape (C, H, W), where C is the number of generated masks. pred_scores (torch.Tensor): An array of length C containing quality scores predicted by the model for each mask. Examples: >>> predictor = Predictor() >>> predictor.setup_model(model_path="sam_model.pt") >>> predictor.set_image("image.jpg") >>> results = predictor(bboxes=[[0, 0, 100, 100]]) bboxespointsmaskslabelsc3K|]}|duV dSNrXrZis r7 z&Predictor.inference..s&::QqDy::::::r8)r0popallgenerateprompt_inference) r3r.rdrergrfmultimask_outputr,kwargss r7 inferencezPredictor.inferences8!!(F33!!(F33   %00!!(F33 ::665"9::: : : 6 4=5d555f55 5$$RHXYYYr8c|j||n|j}||jdd|jddjdd||||}|j|g||RS)aPerform image segmentation inference based on input cues using SAM's specialized architecture. This internal function leverages the Segment Anything Model (SAM) for prompt-based, real-time segmentation. It processes various input prompts such as bounding boxes, points, and masks to generate segmentation masks. Args: im (torch.Tensor): Preprocessed input image tensor with shape (N, C, H, W). bboxes (np.ndarray | list | None): Bounding boxes in XYXY format with shape (N, 4). points (np.ndarray | list | None): Points indicating object locations with shape (N, 2) or (N, num_points, 2), in pixels. labels (np.ndarray | list | None): Point prompt labels with shape (N) or (N, num_points). 1 for foreground, 0 for background. masks (np.ndarray | None): Low-res masks from previous predictions with shape (N, H, W). For SAM, H=W=256. multimask_output (bool): Flag to return multiple masks for ambiguous prompts. Returns: pred_masks (torch.Tensor): Output masks with shape (C, H, W), where C is the number of generated masks. pred_scores (torch.Tensor): Quality scores predicted by the model for each mask, with length C. Examples: >>> predictor = Predictor() >>> im = torch.rand(1, 3, 1024, 1024) >>> bboxes = [[100, 100, 200, 200]] >>> masks, scores, logits = predictor.prompt_inference(im, bboxes=bboxes) Nr<rr)r/get_im_features_prepare_promptsshaper'_inference_features) r3r.rdrergrfrqr/r0s r7rpzPredictor.prompt_inferences404}/D4''+++$-'' djmA6F6LRaR6PRXZ`bhjopp't'M7M.Es2JJJqiia00088::JJJr8axis)r1minr> as_tensor torch_dtyperGndimr@onesrwint32asarrayuint8rcv2 INTER_NEARESTrAtensor) r3 dst_shape src_shaperdrergrfrr\s @r7rvzPredictor._prepare_promptss&.# fCCYq\IaL-H)TU,YbcdYeJe)f)f  _V43CDKXXXF%+[A%5%5VD\\6F~crc!233_V5;t{SSSF<#v|B'7777hFL$4hhU[UabdUehhh877 aKF{a!'4 !3VAAAtG_  _V43CDKXXXF%+[A%5%5VD\\6F aKF  JuBH555E#(:??E$KKE!)%UVfifwxxxIHJJJJEJJJQRSSSELd.>t{SSSEvvu,,r8rg?r @)\(?ffffff?ffffff?c ddl} d|_|jdd\} }t| |f||\}}|t |||}ggggf\}}}}t ||D]\}}|\}}}}||z ||z }}t j||z|j}tj ||gg}tj |d||||f| |fdd }|||z} ggg}#}"}!t|| D]?\}$|||$d \}%}&tj |%d||fdd d}%|&|k}'|%|'|&|'}&}%t|%|jj| }(|(| k}'|%|'|&|'}&}%|%|jjk}%t%|%})t)|)|dd|| g}*t j|*s|)|*|%|*|&|*}&}%})|!|%|#|)|"|&At j|!}!t j|#}#t j|"}"| j|#|"|jj}+t9|#|+|}#t;|!|+|| |}!|"|+}"||!||#||"|||!jdt j|}t j|}t j|}t j|}t?|d kr9d |z },| j||,| }+||+||+||+}}}|||fS) aPPerform image segmentation using the Segment Anything Model (SAM). This method segments an entire image into constituent parts by leveraging SAM's advanced architecture and real-time performance capabilities. It can optionally work on image crops for finer segmentation. Args: im (torch.Tensor): Input tensor representing the preprocessed image with shape (N, C, H, W). crop_n_layers (int): Number of layers for additional mask predictions on image crops. crop_overlap_ratio (float): Overlap between crops, scaled down in subsequent layers. crop_downscale_factor (int): Scaling factor for sampled points-per-side in each layer. point_grids (list[np.ndarray] | None): Custom grids for point sampling normalized to [0,1]. points_stride (int): Number of points to sample along each side of the image. points_batch_size (int): Batch size for the number of points processed simultaneously. conf_thres (float): Confidence threshold [0,1] for filtering based on mask quality prediction. stability_score_thresh (float): Stability threshold [0,1] for mask filtering based on stability. stability_score_offset (float): Offset value for calculating stability score. crop_nms_thresh (float): IoU cutoff for NMS to remove duplicate masks between crops. Returns: pred_masks (torch.Tensor): Segmented masks with shape (N, H, W). pred_scores (torch.Tensor): Confidence scores for each mask with shape (N,). pred_bboxes (torch.Tensor): Bounding boxes for each mask with shape (N, 4). Examples: >>> predictor = Predictor() >>> im = torch.rand(1, 3, 1024, 1024) # Example input image >>> masks, scores, boxes = predictor.generate(im) rNTr<rG.bilinearF)r& align_corners)rerqr) torchvisionr1rwrrzipr>rrGr@arrayF interpolaterrprrJmask_thresholdrrMrrnappendcatr nmsr,iourrexpandr`)-r3r. crop_n_layerscrop_overlap_ratiocrop_downscale_factor point_grids points_stridepoints_batch_size conf_thresstability_score_threshstability_score_offsetcrop_nms_threshrihiw crop_regions layer_idxsrr pred_bboxes region_areas crop_region layer_idxx1y1x2y2wharea points_scalecrop_impoints_for_image crop_masks crop_scores crop_bboxesre pred_mask pred_scoreidxstability_score pred_bbox keep_maskkeepscoress- r7rozPredictor.generateIs)T !""B#6BxPb#c#c j  5m]TijjK=?R^: Kl&), &C&C. B. B "K(NBB7BGqA<Abi888D8aVH--LmBsBrE2b5'8$9B8*dijjjG*95 D 35r2[ J+,=?OPP / / (,(=(=gfgk(=(l(l% :M)D/Aq6 bghhhijk  :-(1# 3: ";tz8:P##&(>>(1# 3: % (AA / ::@@BB 29kAqRTVX>ZZZ y++y7@7KYW`Macmnwcx*yI!!),,,""9---"":....:..J)K00K)K00K?&&{KOOD+K,={KKK%j&6 RLLJ%d+K   j ) ) )   { + + +   { + + +    J,>> predictor = Predictor() >>> predictor.setup_model(model=sam_model, verbose=True) )verboseN)g33333^@gR]@gRY@r:r)g(\2M@g(\L@gL@samrT)rr,rG get_modelrFrLrMevalrJr>rviewrHrIformatstriderK done_warmupfloat16float32r)r3rJrrGs r7 setup_modelzPredictor.setup_models7ty/AAA =NN$$E   $ A EKKMM   L!:!:!:;;@@QJJMMfUU < 7 7 788==b!QGGJJ6RR"  ). ,0JON5==r8c8ddlm}||jjS)zPRetrieve or build the Segment Anything Model (SAM) for image segmentation tasks.r build_sambuildrr,rJr3rs r7rzPredictor.get_model($$$$$$y)))r8c j|dd\}}|jr|dnd}ttdt|jdD}t |t stj|ddddf}g}t|g||j dD]z\} } } | jddkrdtj d|j }} ntj| d| jddd d} | |jjk} |=tj|jdd|| jd }nt'| }tj|jdtj|j } ||jjk} tj||dddf| dddfgd | }| | } |t5| | || | |d |_|S)aPost-process SAM's inference outputs to generate object detection masks and bounding boxes. This method scales masks and boxes to the original image size and applies a threshold to the mask predictions. It leverages SAM's advanced architecture for real-time, promptable segmentation tasks. Args: preds (tuple): The output from SAM model inference, containing: - pred_masks (torch.Tensor): Predicted masks with shape (N, 1, H, W). - pred_scores (torch.Tensor): Confidence scores for each mask with shape (N, 1). - pred_bboxes (torch.Tensor, optional): Predicted bounding boxes if segment_all is True. img (torch.Tensor): The processed input image tensor with shape (C, H, W). orig_imgs (list[np.ndarray] | torch.Tensor): The original, unprocessed images. Returns: (list[Results]): List of Results objects containing detection masks, bounding boxes, and other metadata for each processed image. Examples: >>> predictor = Predictor() >>> preds = predictor.inference(img) >>> results = predictor.postprocess(preds, img, orig_imgs) Nr<c34K|]}t|VdSristrrjs r7rlz(Predictor.postprocess..s(JJ!s1vvJJJJJJr8r.r:rrFpaddingrdimpathnamesrfr{)r1r) enumeraterangerwr=listr convert_torch2numpy_batchrr'r>zerosrG scale_masksrMrJr scale_boxesrarangerr,confrrr )r3predsimg orig_imgsrrrrresultsrforig_imgimg_pathclsrs r7 postprocesszPredictor.postprocesss10#() K"&"2>> predictor = Predictor() >>> predictor.set_image("path/to/image.jpg") >>> predictor.set_image(cv2.imread("path/to/image.jpg")) Notes: - This method should be called before performing inference on a new image. - The extracted features are stored in the `self.features` attribute for later use. Nrz,`set_image` only supports setting one image!)rJr setup_sourcer`datasetrOrur/)r3rWr'r.s r7 set_imagezPredictor.set_images. :        %   4<  A%%%'U%%%\  Eq**B 0044DM EE  r8c<|dSt||jt|jt t fr|jd|jdksJd|jd|j|jdS)z)Set up the data source for SAM inference.Nrrz3SAM models only support square image size, but got r) r*rrr=ratuplerrJ set_imgszr3sourcer6s r7rzPredictor.setup_source3s > F VT[111$*udm44 A$*UV-9W9W9W O$* O O O:X9WW TZ(((((r8c6|j|S)zZExtract image features using the SAM model's image encoder for subsequent mask prediction.)rJ image_encoderr3r.s r7ruzPredictor.get_im_features=sz''+++r8c||_dS)z0Set prompts for subsequent inference operations.N)r0)r3r0s r7 set_promptszPredictor.set_promptsAs  r8c"d|_d|_dS)zQReset the current image and its features, clearing them for subsequent inference.N)r.r/r3s r7 reset_imagezPredictor.reset_imageEs r8cddl}|jddkr|Sg}g}|D]}|t j}t||d\}}| }t||d\}}|o| }|tj | d|t|tj |d}t|} |j| tj ||} || |j|j| fS)aWRemove small disconnected regions and holes from segmentation masks. This function performs post-processing on segmentation masks generated by the Segment Anything Model (SAM). It removes small disconnected regions and holes from the input masks, and then performs Non-Maximum Suppression (NMS) to eliminate any newly created duplicate boxes. Args: masks (torch.Tensor): Segmentation masks to be processed, with shape (N, H, W) where N is the number of masks, H is height, and W is width. min_area (int): Minimum area threshold for removing disconnected regions and holes. Regions smaller than this will be removed. nms_thresh (float): IoU threshold for the NMS algorithm to remove duplicate boxes. Returns: new_masks (torch.Tensor): Processed masks with small regions removed, shape (N, H, W). keep (list[int]): Indices of remaining masks after NMS, for filtering corresponding boxes. Examples: >>> masks = torch.rand(5, 640, 640) > 0.5 # 5 random binary masks >>> new_masks, keep = remove_small_regions(masks, min_area=100, nms_thresh=0.7) >>> print(f"Original masks: {masks.shape}, Processed masks: {new_masks.shape}") >>> print(f"Indices of kept masks: {keep}") rNholesr&islandsrrGr)rrwcpunumpyastyper@rrrr>r unsqueezerMrrr rrFrGr) rfmin_area nms_threshr new_masksrmaskchanged unchangedr{rs r7rzPredictor.remove_small_regionsJsj2  ;q>Q  L  , ,D88::##%%,,RX66D0xgNNNMD'# I0xiPPPMD'!1'kI   U_T22<rrGr rrMrJrrrrr)r3r/rrrdrergrfrqr0rrrrs r7inference_featureszPredictor.inference_features}s<DC$)/49?!C '' 9fffV[\\":$":8"`g"`O_"`"`"` K  A ! # #&*EKzGX,Y,Y,Y JJD)9)?)?)A)A9V[\\\]^_J#dj&??J-j99K,z/2%+jN_```C)[+aaag2FAAAtG $U[]^^^K;&&r8r r!)NNNNFNNNN) rrrNrrrrrrNT)rrNNNNNF)__name__ __module__ __qualname____doc__rr r+rOrBrsrprxrvrorrrrrrurr" staticmethodrrr3 __classcell__r6s@r7rr+s''RF&$RV!!!!!!!,   D0004$Z$Z$Z$ZLNNNND&C&C&C&CP1-1-1-1-l%##m4m4m4m4^OOOOB*** 222h@))))),,, 0P0P0P\0Pd -'-'-'-'-'-'-'-'r8rcXeZdZdZgdZdZdZd fd ZfdZdZ d d Z xZ S) SAM2PredictoraSAM2Predictor class for advanced image segmentation using Segment Anything Model 2 architecture. This class extends the base Predictor class to implement SAM2-specific functionality for image segmentation tasks. It provides methods for model initialization, feature extraction, and prompt-based inference. Attributes: _bb_feat_sizes (list[tuple]): Feature sizes for different backbone levels. model (torch.nn.Module): The loaded SAM2 model. device (torch.device): The device (CPU or GPU) on which the model is loaded. features (dict): Cached image features for efficient inference. segment_all (bool): Flag to indicate if all segments should be predicted. prompts (dict[str, Any]): Dictionary to store various types of prompts for inference. Methods: get_model: Retrieve and initialize the SAM2 model. prompt_inference: Perform image segmentation inference based on various prompts. set_image: Preprocess and set a single image for inference. get_im_features: Extract and process image features using SAM2's image encoder. Examples: >>> predictor = SAM2Predictor(cfg) >>> predictor.set_image("path/to/image.jpg") >>> bboxes = [[100, 100, 200, 200]] >>> result = predictor(bboxes=bboxes)[0] >>> print(f"Predicted {len(result.masks)} masks with average score {result.boxes.conf.mean():.2f}") ))rA)rB)rrrc8ddlm}||jjS)zYRetrieve and initialize the Segment Anything Model 2 (SAM2) for image segmentation tasks.rrrrs r7rzSAM2Predictor.get_modelrr8Nct||||||\}}}}||ddd}tjddggtj|j|jdd}|1tj ||gd}tj ||gd}n||}}|||fS) aiPrepare and transform the input prompts for processing based on the destination shape. Args: dst_shape (tuple[int, int]): The target shape (height, width) for the prompts. src_shape (tuple[int, int]): The source shape (height, width) of the input image. bboxes (np.ndarray | list | None): Bounding boxes in XYXY format with shape (N, 4). points (np.ndarray | list | None): Points indicating object locations with shape (N, 2) or (N, num_points, 2), in pixels. labels (np.ndarray | list | None): Point prompt labels with shape (N,) or (N, num_points). 1 for foreground, 0 for background. masks (list | np.ndarray | None): Masks for the objects, where each mask is a 2D array. Returns: points (torch.Tensor | None): Transformed points. labels (torch.Tensor | None): Transformed labels. masks (torch.Tensor | None): Transformed masks. Raises: AssertionError: If the number of points don't match the number of labels, in case labels were passed. Nr:r<r;rrrr) r*rvrr>rrrGrrwr) r3rrrdrergrf bbox_labelsr6s r7rvzSAM2Predictor._prepare_promptss*).(@(@IW]_egmot(u(u%  [[Q**F,Axu{6=YYY``agamnoaprtuuK!FF#3;;;K#8a@@@!'vu$$r8crt|fddD_dS)z9Set up the data source and image size for SAM2 inference.c:g|]fdjDS)cDg|]}t|jzz SrXintrrZr[rkr3s r7r]z9SAM2Predictor.setup_source...s,OOOqAq$9 : :OOOr8rarZrkr3s @r7r]z.SAM2Predictor.setup_source..s5kkkTUOOOOODJOOOkkkr8g??rN)r*r_bb_feat_sizesrs` r7rzSAM2Predictor.setup_sources? V$$$kkkkYjkkkr8c |j|}|j|\}}}}|jjr|d|jjz|d<dt ||jD}|d|dddS)zLExtract image features from the SAM image encoder for subsequent processing.r:c\g|])\}}|dddjddg|R*S)rr<rr:)permuterrZfeat feat_sizes r7r]z1SAM2Predictor.get_im_features..sQ   >MdI &DLLAq ! ! &q" 9y 9 9 9   r8N) image_embedhigh_res_feats)rJ forward_image_prepare_backbone_featuresdirectly_add_no_mem_embed no_mem_embedrrP)r3r. backbone_out_ vision_featsfeatss r7ruzSAM2Predictor.get_im_featuressz//33 $ E El S S<A : / J+B/$*2IIL   QTUacgcvQwQw    %RyE#2#JGGGr8Fr:c |||fnd}|j|d|\}}|duo|djddk} d} t|tr#fd|dD} |dg}|j||jj|||| | \} } } } | dd| ddfS) avPerform inference on image features using the SAM2 model. Args: features (torch.Tensor | dict[str, Any]): Extracted image features with shape (B, C, H, W) from the SAM2 model image encoder. Can also be a dict with 'image_embed' (torch.Tensor) of shape (B, C, H, W) and 'high_res_feats' (list[torch.Tensor]) high-resolution feature maps from the backbone. points (np.ndarray | list[list[float]] | None): Object location points with shape (N, 2), in pixels. labels (np.ndarray | list[int] | None): Point prompt labels with shape (N,). 1 = foreground, 0 = background. masks (list[np.ndarray] | np.ndarray | None): Masks for the objects, where each mask is a 2D array. multimask_output (bool): Flag to return multiple masks for ambiguous prompts. img_idx (int): Index of the image in the batch to process. Returns: pred_masks (torch.Tensor): Output masks with shape (C, H, W), where C is the number of generated masks. pred_scores (torch.Tensor): Quality scores for each mask, with length C. NrzrrcFg|]}|dS)r)r+)rZ feat_levelimg_idxs r7r]z5SAM2Predictor._inference_features..,s, o o ojG!4!>!>q!A!A o o or8rXrW)r|r}r~rrq repeat_imagehigh_res_features)rJsam_prompt_encoderrwr=r)sam_mask_decoderrr)r3r/rergrfrqrdrr batched_moderfrrr^s ` r7rxz!SAM2Predictor._inference_features s*2&,%7&&!!T.2j.K.K/L/ / ++ T)DfQioa.@1.D   h % % : o o o oT\]mTn o o o  .y9H(, (C(C%Z2??AA%6$4-%/)D) ) % KA!!!Q'')<)s@r7r@r@s6N F*** % % % % % %Dlllll H H H0C0C0C0C0C0C0C0Cr8r@ceZdZdZeddfd fd ZfdZd!dZfdZe d"d#d Z e d$d#d Z e dZ e dZd%dZd$d#dZ d&d#dZd$d#dZ d'd#dZd$d#dZ d$d#dZ d$d#dZd$d#dZe d(dZe dZe dZe dZd$dZxZS))SAM2VideoPredictoraSAM2VideoPredictor to handle user interactions with videos and manage inference states. This class extends the functionality of SAM2Predictor to support video processing and maintains the state of inference operations. It includes configurations for managing non-overlapping masks, clearing memory for non-conditional inputs, and setting up callbacks for prediction events. Attributes: inference_state (dict): A dictionary to store the current state of inference operations. non_overlap_masks (bool): A flag indicating whether masks should be non-overlapping. clear_non_cond_mem_around_input (bool): A flag to control clearing non-conditional memory around inputs. clear_non_cond_mem_for_multi_obj (bool): A flag to control clearing non-conditional memory for multi-object scenarios. callbacks (dict): A dictionary of callbacks for various prediction lifecycle events. Methods: get_model: Retrieve and configure the model with binarization enabled. inference: Perform image segmentation inference based on the given input cues. postprocess: Post-process the predictions to apply non-overlapping constraints if required. add_new_prompts: Add new points or masks to a specific frame for a given object ID. propagate_in_video_preflight: Prepare inference_state and consolidate temporary outputs before tracking. init_state: Initialize an inference state for the predictor. get_im_features: Extract image features using SAM2's image encoder for subsequent segmentation tasks. Examples: >>> predictor = SAM2VideoPredictor(cfg=DEFAULT_CFG) >>> predictor.set_image("path/to/video_frame.jpg") >>> bboxes = [[100, 100, 200, 200]] >>> results = predictor(bboxes=bboxes) Notes: The `fill_hole_area` attribute is defined but not used in the current implementation. Nr r!ct|||i|_d|_d|_d|_|jd|jd|_ dS)aKInitialize the predictor with configuration and optional overrides. This constructor initializes the SAM2VideoPredictor with a given configuration, applies any specified overrides, and sets up the inference state along with certain flags that control the behavior of the predictor. Args: cfg (dict): Configuration dictionary containing default settings. overrides (dict | None): Dictionary of values to override default configuration. _callbacks (dict | None): Dictionary of callback functions to customize behavior. TFon_predict_startN) r*r+inference_statenon_overlap_masksclear_non_cond_mem_around_input clear_non_cond_mem_for_multi_obj callbacksr init_stateclear_non_cond_memr2s r7r+zSAM2VideoPredictor.__init__`sm i444!!%/4,05- )*11$/BBB"&r8crt}|d|S)zRetrieve and configure the model with binarization enabled. Notes: This method overrides the base class implementation to set the binarize flag to True. T)r*r set_binarize)r3rJr6s r7rzSAM2VideoPredictor.get_modelss3 !!## 4    r8c |jd|}|jd|}|jd|}|jj}||jd<|jd}t |ddkr||jdd |jd djd d||||\}}}|Gtt |D])}| |||g||g| *n@|>tt |D]!}| |||g| "| |jd } t |jd} t |ddkrtd|| dvr:d} || |} |j r"|js| d kr||nZ|| dvrd} || |} n?d} |||| dd d dd} | || |<||||| | |jd|| ddd } | | |jjkddk} | t1j| jd| j| jfS)abPerform image segmentation inference based on the given input cues, using the currently loaded image. This method leverages SAM's (Segment Anything Model) architecture consisting of image encoder, prompt encoder, and mask decoder for real-time and promptable segmentation tasks. Args: im (torch.Tensor): The preprocessed input image in tensor format, with shape (N, C, H, W). bboxes (np.ndarray | list, optional): Bounding boxes with shape (N, 4), in XYXY format. points (np.ndarray | list, optional): Points indicating object locations with shape (N, 2), in pixels. labels (np.ndarray | list, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background. masks (np.ndarray, optional): Low-resolution masks from previous predictions shape (N,H,W). For SAM H=W=256. Returns: pred_masks (torch.Tensor): The output masks in shape CxHxW, where C is the number of generated masks. pred_scores (torch.Tensor): An array of length C containing predicted quality scores for each mask. rdrerfr. output_dictcond_frame_outputsrr<Nr)obj_idrerg frame_idx)rzrfr{consolidated_frame_inds obj_idx_to_id/No points are provided; please add points firstnon_cond_frame_outputsFT)rxr{ batch_sizeis_init_cond_frame point_inputs mask_inputsreverserun_mem_encoderframes_already_trackedrrr<r)r0rmrframernr`rvrwr'radd_new_promptspropagate_in_video_preflight RuntimeErrorrprq _clear_non_cond_mem_around_input_run_single_frame_inference_prune_non_cond_memory_add_output_per_objectrrrJrsumr>rrrG)r3r.rdrergrfrrxrkr|r storage_key current_outrs r7rszSAM2VideoPredictor.inference}sX"!!(F33!!(F33   %00 "%'T"*=9 {/0 1 1Q 6 6$($9$9 djmA.4RaR8&&&RW%% !FFE!s6{{++llA((&!+fVWUXkej(kkkkl"s5zz**VVA((su(UUUU ))+++"&"67P"Q-o>?? {/0 1 1Q 6 6PQQ Q +,@A A A.K%k259K3 =9^ =blpqbqbq55e<<< -.FG G G2K%k259KK2K::'%#(!  $;  K/:K $U +  ' ' . . . ##E; DDD 56==eDDD .66q!<< dj.G!G L LV T TWX XY 5:j&6q&9AQZdZklllllr8c4t|||}|jrl|D]i}|jt |jdkr"|j|jjdd|j_j|S)a3Post-process the predictions to apply non-overlapping constraints if required. This method extends the post-processing functionality by applying non-overlapping constraints to the predicted masks if the `non_overlap_masks` flag is set to True. This ensures that the masks do not overlap, which can be useful for certain applications. Args: preds (tuple[torch.Tensor, torch.Tensor]): The predicted masks and scores from the model. img (torch.Tensor): The processed image tensor. orig_imgs (list[np.ndarray]): The original images before processing. Returns: (list): The post-processed predictions. Notes: If `non_overlap_masks` is True, the method applies constraints to ensure non-overlapping masks. Nr) r*rrorfr`rJ"_apply_non_overlapping_constraintsdatar+)r3r r r r resultr6s r7rzSAM2VideoPredictor.postprocesss$''%%eS)<<  ! u! u u<'3v|+<+<+A+A$(J$Q$QRXR^RcRmRmnoRpRp$q$qrs$t !!r8rrndict[str, Any] | Nonec  |p|j}|du|duz s Jd|||}d}d} |||d}||d||<d} ||d||<|| ||d||dv} |d|} |d|} | p |jj} | rd nd }d}|| ||p5| d |p| d |}|[|d F|d |j|jjd k }| dd| | |d| ||dd|| }|| ||<| || d|}|d  dd}| ddtjd|j|jfS)avAdd new points or masks to a specific frame for a given object ID. This method updates the inference state with new prompts (points or masks) for a specified object and frame index. It ensures that the prompts are either points or masks, but not both, and updates the internal state accordingly. It also handles the generation of new segmentations based on the provided prompts and the existing state. Args: obj_id (int): The ID of the object to which the prompts are associated. points (torch.Tensor, optional): The coordinates of the points of interest. labels (torch.Tensor, optional): The labels corresponding to the points. masks (torch.Tensor, optional): Binary masks for the object. frame_idx (int, optional): The index of the frame to which the prompts are applied. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: pred_masks (torch.Tensor): The flattened predicted masks. pred_scores (torch.Tensor): A tensor of ones indicating the number of objects. Raises: AssertionError: If both `masks` and `points` are provided, or neither is provided. Notes: - Only one type of prompt (either points or masks) can be added per call. - If the frame is being tracked for the first time, it is treated as an initial conditioning frame. - The method handles the consolidation of outputs and resizing of masks to the original video resolution. Nz@'masks' and 'points' prompts are not compatible with each other.point_inputs_per_obj point_coords point_labelsmask_inputs_per_objroutput_dict_per_objtemp_output_dict_per_objryrrcuda)rG non_blockingg@g@@rF) rxr{rrrrrrprev_sam_mask_logitsrnis_condrrnrr)rn_obj_id_to_idxrmrJ!add_all_frames_to_correct_as_condgetrFrGtypeclamp_r#_consolidate_temp_output_across_objrr>rr)r3rzrergrfr{rnobj_idxrpop_keyrobj_output_dictobj_temp_output_dictrrrprev_outrconsolidated_outrs r7rz"SAM2VideoPredictor.add_new_promptssL*AT-A &D.1uu3uuu1%%fo>> (  ,2FKKLJVO2 3G >> 'o>V.WW)*?@I./IJ7S%T (T.5S**;S  $  #$[155i@@L"#78<FTryrrrnrNrrr)rnr`setvaluesr(keysrrrprqrclearrmdiscard)r3rnrrrxr|rrtemp_frame_indsrr{rrall_consolidated_frame_indsinput_frames_indspoint_inputs_per_framemask_inputs_per_frames r7rz/SAM2VideoPredictor.propagate_in_video_preflightMso*AT-A26./9:: $33M#N %m4 #22K"L$ : :G29W..?WK"eeO(@(G(G(I(I Q Q$&&';K'H'M'M'O'OPPPP #K 0 7 7 H H H, E E #'#K#KwVe$L$$ 7G K(3++I7Gfu+vvv7ET=bEfptufufu99)DDD)A(G(G(I(I : :$$[1779999 : %%9: G GI 0 1 5 5i F F F F./DELLNN O OO,-AB O O  89==iNNNN O01EF Q QI ,@ AAAAA #$< = E Ei P P P P $$8 9OO ,V 48>OO ,W 5)-oo.N)O)OOI &?AO2 3G <>@O1 27 ;&(*,??O1 27 ; ')*,DDO6 7 @NFFFF,;I,FFFF r8c ~| p|j} || d|\} } } ||J|j||| | | |||| d|||  }|d}|8|t j|j|jjdk|d<| |d| |d<|S) aRun tracking on a single frame based on current inputs and previous memory. Args: output_dict (dict): The dictionary containing the output states of the tracking process. frame_idx (int): The index of the current frame. batch_size (int): The batch size for processing the frame. is_init_cond_frame (bool): Indicates if the current frame is an initial conditioning frame. point_inputs (dict | None): Input points and their labels. mask_inputs (torch.Tensor | None): Input binary masks. reverse (bool): Indicates if the tracking should be performed in reverse order. run_mem_encoder (bool): Indicates if the memory encoder should be executed. prev_sam_mask_logits (torch.Tensor | None): Previous mask logits for the current object. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: (dict): A dictionary containing the output of the tracking step, including updated features and predictions. Raises: AssertionError: If both `point_inputs` and `mask_inputs` are provided simultaneously. Notes: - The method assumes that `point_inputs` and `mask_inputs` are mutually exclusive. - The method retrieves image features using the `get_im_features` method. - The `maskmem_pos_enc` is assumed to be constant across frames, hence only one copy is stored. - The `fill_holes_in_mask_scores` function is commented out and currently unsupported due to CUDA extension requirements. r.Nr r{rcurrent_vision_featscurrent_vision_pos_embedsrrrrxrtrack_in_reverserrmaskmem_featuresrrrGrmaskmem_pos_enc) rnrurJ track_steprFr>rrGr_get_maskmem_pos_enc)r3rxr{rrrrrrrrnrrrrrs r7rz.SAM2VideoPredictor._run_single_frame_inference s P*AT-AFJFZFZ D !:G G C7 #{':'::j++1!5&?!%##&|4$+!5,   ''9:  '.>.A.AmDKdkFVZ`F`/B//K* +*.)B)B;O`Cacr)s)s %&r8c|p|j}|d}|\d|vr)t|tsJd|D}||d<n|d}|djddkrfd|D}|S)aCache and manage the positional encoding for mask memory across frames and objects. This method optimizes storage by caching the positional encoding (`maskmem_pos_enc`) for mask memory, which is constant across frames and objects, thus reducing the amount of redundant information stored during an inference session. It checks if the positional encoding has already been cached; if not, it caches a slice of the provided encoding. If the batch size is greater than one, it expands the cached positional encoding to match the current batch size. Args: out_maskmem_pos_enc (list[torch.Tensor] | None): The positional encoding for mask memory. Should be a list of tensors or None. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: (list[torch.Tensor]): The positional encoding for mask memory, either cached or expanded. Notes: - The method assumes that `out_maskmem_pos_enc` is a list of tensors or None. - Only a single object's slice is cached since the encoding is the same across objects. - The method checks if the positional encoding has already been cached in the session's constants. - If the batch size is greater than one, the cached encoding is expanded to fit the batch size. rNrcFg|]}|ddS)Nr)clone)rZr[s r7r]z;SAM2VideoPredictor._get_maskmem_pos_enc..s("N"N"NQ1RaR5;;=="N"N"Nr8rrc@g|]}|dddS)r:)r)rZr[rs r7r]z;SAM2VideoPredictor._get_maskmem_pos_enc..s+&a&a&aAqxx BB'G'G&a&a&ar8)rnr=rrw)r3out_maskmem_pos_encrnmodel_constantsrrs @r7rz'SAM2VideoPredictor._get_maskmem_pos_encns0*AT-A)+6  * 77!"5t<<<<<"N"N:M"N"N"N5D 122"12C"D,Q/5a8JA~~&a&a&a&aQ`&a&a&a#""r8Fc |p|j}t|d}|rdnd}ddtj|dg|jdRd|j|jtj||jjfd|j|jtj|dfd |j|jd }t|D]}|d |} |d |} | | |p5| d |p| d |} | &|r#| ||d ||dz<| d|d||dz<| d |d ||dz<|rrtj |d|jdd} |jjr|j| } ||| d|d|\|d<|d<|S)aConsolidate per-object temporary outputs into a single output for all objects. This method combines the temporary outputs for each object on a given frame into a unified output. It fills in any missing objects either from the main output dictionary or leaves placeholders if they do not exist in the main output. Optionally, it can re-run the memory encoder after applying non-overlapping constraints to the object scores. Args: frame_idx (int): The index of the frame for which to consolidate outputs. is_cond (bool, optional): Indicates if the frame is considered a conditioning frame. run_mem_encoder (bool, optional): Specifies whether to run the memory encoder after consolidating the outputs. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: (dict): A consolidated output dictionary containing the combined results for all objects. Notes: - The method initializes the consolidated output with placeholder values for missing objects. - It searches for outputs in both the temporary and main output dictionaries. - If `run_mem_encoder` is True, it applies non-overlapping constraints and re-runs the memory encoder. - The `maskmem_features` and `maskmem_pos_enc` are only populated when `run_mem_encoder` is True. r}ryrNrrsize fill_valuerrG$@rrrobj_ptrobject_score_logitsrrrrrFrr&rTr)rhigh_res_masksis_mask_from_ptsrrnrr)rnr`r>fullrPrrGrJ hidden_dimrr_get_empty_mask_ptrrrranon_overlap_masks_for_mem_encr_run_memory_encoder) r3r{rrrnrrrrrroutrs r7rz6SAM2VideoPredictor._consolidate_temp_output_across_objs>*AT-A9:: .5S**;S !%#* !=d&9!&<=="&{ z $*"78"&{  $): !_ &{ $$$!  2Z(( P PG#23M#Nw#W -.CDWMO$[155i@@L ##78< >  ] .Z# N z7 _!%!N!N~!^!^X\XpXp%-!%$45J$K / YqYY U / 02BCT2U r8c |p|j}||d\}}}|j|d|||dt jddg|jR|j|ji|dddd }|d S) aGet a dummy object pointer based on an empty mask on the current frame. Args: frame_idx (int): The index of the current frame for which to generate the dummy object pointer. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: (torch.Tensor): A tensor representing the dummy object pointer generated from the empty mask. r.TNrrrFrr) rnrurJrr>rrarrG)r3r{rnrrrrs r7rz&SAM2VideoPredictor._get_empty_mask_ptrs*AT-AFJFZFZ[jko[pFqFqC7j++#!5&?! Q$7DJ$7$7t?OX\Xcddd&|4"!!%,  9%%r8c2|p|j}||d|\}}}|j|||||\} } || |} | t j|j|jj dk| fS)aRun the memory encoder on masks. This is usually after applying non-overlapping constraints to object scores. Since their scores changed, their memory also needs to be computed again with the memory encoder. Args: batch_size (int): The batch size for processing the frame. high_res_masks (torch.Tensor): High-resolution masks for which to compute the memory. object_score_logits (torch.Tensor): Logits representing the object scores. is_mask_from_pts (bool): Indicates if the mask is derived from point interactions. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. Returns: maskmem_features (torch.Tensor): The encoded mask features. maskmem_pos_enc (torch.Tensor): The positional encoding. r.)rrpred_masks_high_resrrrr) rnrurJ_encode_new_memoryrrFr>rrGr) r3rrrrrnrr^rrrs r7rz&SAM2VideoPredictor._run_memory_encoder's2*AT-A.2.B.B?SWCXZd.e.e+a,0J,I,I!5! .- 3 -J- - )/33O_UU""- $+BRV\B\#   r8c |p|j}|d}|t|tjsJ|d}|t|tsJ|dD]b\}}t ||dz dd|d |d d} | | | d<| fd |D| d<| |||<cdS) aISplit a multi-object output into per-object output slices and add them into Output_Dict_Per_Obj. The resulting slices share the same tensor storage. Args: frame_idx (int): The index of the current frame. current_out (dict): The current output dictionary containing multi-object outputs. storage_key (str): The key used to store the output in the per-object output dictionary. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. rNrrrrr)rrrrc g|] }| SrXrX)rZr[ obj_slices r7r]z=SAM2VideoPredictor._add_output_per_object..qs-T-T-Tqa l-T-T-Tr8)rnr=r>r?ritemsslice) r3r{rrrnrrrrobj_outrs @r7rz)SAM2VideoPredictor._add_output_per_objectQs *AT-A&'9:':6F +U+U''U%&78&*_d*K*K&&K(78M(N(T(T(V(V > > $G_gw{33I$(#'),7 B&y1)< G  +.>y.I*+*-T-T-T-TO-T-T-T)*6=OK ( 3 3 > >r8cZ|p|j}|jj}|||jjzz }|||jjzz}t ||dzD]]}|dd|d|dD]}|d|d^dS)aRemove the non-conditioning memory around the input frame. When users provide correction clicks, the surrounding frames' non-conditioning memories can still contain outdated object appearance information and could confuse the model. This method clears those non-conditioning memories surrounding the interacted frame to avoid giving the model both old and new information about the object. Args: frame_idx (int): The index of the current frame where user interaction occurred. inference_state (dict[str, Any], optional): The current inference state. If None, uses the instance's inference state. rrxrNr)rnrJmemory_temporal_stride_for_eval num_maskmemrrmr)r3r{rnrframe_idx_begin frame_idx_endtrs r7rz3SAM2VideoPredictor._clear_non_cond_mem_around_inputts*AT-A J 6#a$**@&@@!A (>$>>  (9:: G GA M *+C D H HD Q Q Q#23H#I#P#P#R#R G G 89==aFFFF G G Gr8c d|d}|&|sdStd|dddtddkrdSt }|d||d ||D]}||d ttt   | fd D}ttt|}tt| tt||d<tt||d <|d< fd } | d| d | d | dfd} | dd| dddS)zRemove an object id from the tracking state. If strict is True, we check whether the object id actually exists and raise an error if it doesn't exist. rNrzCannot remove object id z/ as it doesn't exist. All existing object ids: rrrrc g|] }| SrXrX)rZold_idx old_obj_idss r7r]z4SAM2VideoPredictor.remove_object..sOOO{7+OOOr8r}cg}D]8}||}|vr|||f9||dSri)rmrr() containernew_kvskvold_idx_to_new_idx old_obj_indss r7 _map_keysz3SAM2VideoPredictor.remove_object.._map_keysslG! ? ?MM!$$***NN$6q$91#=>>>   W % % % % %r8rrcn||D]\}}|d|d<fd|dD|d<|d|d<|d|d<|d|d<|d|d<|||dS)Nrc g|] }| SrXrX)rZr[remain_old_obj_indss r7r]zJSAM2VideoPredictor.remove_object.._slice_state..s)a)a)aQ!,?*@)a)a)ar8rrrrr)rrr)rxrr{rrnrr3s r7 _slice_statez6SAM2VideoPredictor.remove_object.._slice_states"-k":"@"@"B"B j j 3*-.@*ABU*V&')a)a)a)a#N_J`)a)a)a%&)-)B)B3GXCY[j)k)k%&$' $56I$JL!!$Y0C!DI-01F-GH[-\)*++IsKYh+iiii j jr8rxryr) rrr`clear_all_points_in_videorr(clear_all_points_in_framerrcopyremover)r)r3rnrzstrictold_obj_idx_to_rmobj_input_frames_indsr{ new_obj_ids new_obj_indsrrr rr rs`` @@@@r7 remove_objectz SAM2VideoPredictor.remove_objects ,O<@@NN  $ 2&y11J6JJ,;I,FJJJ  / 0 0A 5 5  * *? ; ; ;"9- -!$$$_5K%LM^%_```$$_5J%KL]%^___. O OI  * *?Iv N N N N&i0 E#k"2"23344 *//11""#4555OOOO;NOOO E#k"2"23344 !#&9<"H"HII+/K0N0N+O+O(+/L+0N0N+O+O(%0 "  & & & & & &  /"89::: /"78999 /"78999 /"<=>>> j j j j j j j  _]35IJJJ _]35MNNNy))r8c|||}|d||d|d||d|d}||d|d||d|dt|d}d}t|D]*}||d|vrd }n||d|vrd }n+|s|d } |d } | d|| d|| d|d} | '| | d|<|d |dt|D]9}|d |} | d|d} | | | d|<:t| ddkr||dSdSdS)zGRemove all input points or mask in a specific frame for a given object.rNrrryrr}FTrxr|rrr)rrmr`rr_reset_tracking_results)r3rnr{rzrrrframe_has_inputobj_idx2rxr|rrrs r7rz,SAM2VideoPredictor.clear_all_points_in_framesh%%fo>> ./8<?CCItTTT )*BCGG SWXXX9:: j))  HO,BCHMMM"&O,AB8LLL"&M  >)-8K&56O&P # #$8 9 A A) L L L #$< = E Ei P P P2377 4HHCDG 45i@ 89==iNNN!*-- S S"12G"H"R)*>?CCItTT&KRO$<=iH;3455::,,_=====+ > >(;:r8c|||d|d|d|d|d|d|ddS) zCRemove all input points or mask in all frames throughout the video.rr}rrrrrN)rr)r3rns r7rz,SAM2VideoPredictor.clear_all_points_in_video s $$_555(..000(..000 "((***./55777-.44666-.446662399;;;;;r8cV|dD]}||dD]}||dD]6}|d|d7|dD]6}|d|d7|dd|dd|dd|ddd |d <|d d |d <d S)z8Reset all tracking inputs and results across the videos.rrrryrrrxr|FrrNfirst_ann_frame_idx)rr)rnr s r7rz*SAM2VideoPredictor._reset_tracking_resultss!!78??AA  A GGIIII !67>>@@  A GGIIII !67>>@@ 0 0A " # ) ) + + + & ' - - / / / / !;<CCEE 0 0A " # ) ) + + + & ' - - / / / / &';<BBDDD &'?@FFHHH123GHNNPPP123KLRRTTT27./017799915-...r8c|jsdS|p|j}||jj|jjzz |d}fd|dDD]}|d|d|diD]5}fd|dDD]}|d|d6dS)z8Prune old non-conditioning frames to bound memory usage.Nrxc g|] }|k| SrXrXrZr  min_frames r7r]z=SAM2VideoPredictor._prune_non_cond_memory..7sTTTa)mm!mmmr8rrc g|] }|k| SrXrXr&s r7r]z=SAM2VideoPredictor._prune_non_cond_memory..<s\\\AaR[mmammmr8)rtrnrJrrrmrr)r3r{rnrxfrr's @r7rz)SAM2VideoPredictor._prune_non_cond_memory,s&  F)AT-A 69c cc %m4 UTTT[)ABTTT ? ?A 0 1 5 5a > > > > /223H"MMTTVV G GO\\\\1I!J\\\ G G 89==aFFFF G G Gr8r4r5)NNNrN)rnrrirNN)FFNF)r8r9r:r;r r+rrsrrrrr<rsrrurrrrrrrrrrrrrr=r>s@r7rkrk<s'F'$RV'''''''&GmGmGmGmR415jijijijijiXH@H@H@H@H@TTT\T ''\'R4444.44444@"15LLLLL\'#'#'#'#'#X15 m m m m m ^&&&&&N26 (((((V]a!>!>!>!>!>FGGGGG,L*L*L*L*\.>.>.>` < < <66\6(GGGGGGGGr8rkceZdZdZedddfd.fd Ze d/d0dZd1dZe d2d3d!Z d4d%Z d5d&Z d6d(Z d2d7d-Z xZS)8SAM2DynamicInteractivePredictoraSAM2DynamicInteractivePredictor extends SAM2Predictor to support dynamic interactions with video frames or a sequence of images. Attributes: memory_bank (list): OrderedDict: Stores the states of each image with prompts. obj_idx_set (set): A set to keep track of the object indices that have been added. obj_id_to_idx (OrderedDict): Maps object IDs to their corresponding indices. obj_idx_to_id (OrderedDict): Maps object indices to their corresponding IDs. Methods: get_model: Retrieves and configures the model with binarization enabled. inference: Performs inference on a single image with optional prompts and object IDs. postprocess: Post-processes the predictions to apply non-overlapping constraints if required. update_memory: Append the imgState to the memory_bank and update the memory for the model. track_step: Tracking step for the current image state to predict masks. get_maskmem_enc: Get memory and positional encoding from the memory bank. Examples: >>> predictor = SAM2DynamicInteractivePredictor(cfg=DEFAULT_CFG) >>> predictor(source=support_img1, bboxes=bboxes1, obj_ids=labels1, update_memory=True) >>> results1 = predictor(source=query_img1) >>> predictor(source=support_img2, bboxes=bboxes2, obj_ids=labels2, update_memory=True) >>> results2 = predictor(source=query_img2) Nr;r4rr5r max_obj_numrJr r!returnNonect|||d|_g|_t |_t tt|x|_ |_ ||_ dS)aInitialize the predictor with configuration and optional overrides. This constructor initializes the SAM2DynamicInteractivePredictor with a given configuration, applies any specified overrides Args: cfg (Any): Configuration dictionary containing default settings. overrides (dict[str, Any] | None): Dictionary of values to override default configuration. max_obj_num (int): Maximum number of objects to track. Default is 3. This is set to keep fixed feature size for the model. _callbacks (dict | None): Dictionary of callback functions to customize behavior. TN) r*r+ro memory_bankr obj_idx_setrrrrr} _max_obj_num)r3r4r5r/r r6s r7r+z(SAM2DynamicInteractivePredictor.__init__Zsw& i444!%552=ikHZHZ>[>[2\2\\T/'r8Fr.torch.Tensor | np.ndarrayrdlist[list[float]] | Nonerf torch.Tensor | np.ndarray | Nonererglist[int] | Noner update_memorybool!tuple[torch.Tensor, torch.Tensor]c.||||j|jddjdd||||\}}}|rt |t r|g}| Jd| | Jd|ftjt|ddf|j |j }tjt|dftj |j }|*t|t|ks Jd t|t|ks Jd | |||||}|d |d } } t|jdkrt!d t#|j} | | | | } } tj| dz d} | dd| ddfS)aoPerform inference on a single image with optional bounding boxes, masks, points and object IDs. It has two modes: one is to run inference on a single image without updating the memory, and the other is to update the memory with the provided prompts and object IDs. When update_memory is True, it will update the memory with the provided prompts and obj_ids. When update_memory is False, it will only run inference on the provided image without updating the memory. Args: im (torch.Tensor | np.ndarray): The input image tensor or numpy array. bboxes (list[list[float]] | None): Optional list of bounding boxes to update the memory. masks (torch.Tensor | np.ndarray | None): Optional masks to update the memory. points (list[list[float]] | None): Optional list of points to update the memory, each point is [x, y]. labels (list[int] | None): Optional list of labels for point prompts (>0 for positive, 0 for negative). obj_ids (list[int] | None): Optional list of object IDs corresponding to the prompts. update_memory (bool): Flag to indicate whether to update the memory with new objects. Returns: res_masks (torch.Tensor): The output masks in shape (C, H, W) object_score_logits (torch.Tensor): Quality scores for each mask rrNr<)rrrerdrgrfz3obj_ids must be provided when update_memory is TruezDbboxes, masks, or points must be provided when update_memory is Truerz,masks and obj_ids must have the same length.z-points and obj_ids must have the same length.rrzMNo objects have been added to the state. Please add objects before inference.r)r)rurvrar'rwr=rJr>rr`rrGrr:rr4rrrr) r3r.rdrfrergrr:rrrrs r7rsz)SAM2DynamicInteractivePredictor.inferenceys,< R   $ 5 5jjmA&,RaR0 !6! !   ?'3'' $")&&(]&&&$(:(:V);(::~c'llAq%9AQZ^Zefffc'llA%6ekRVR]^^^ 5zzS\\1113a111v;;#g,,...0_...   w > > >oo'' "-l";[I^=_K t A % %noo o4#$$",S/;s3CK l;#3;;; !!!Q'')<).s^" " " i 'DLLAq ! ! & C 122 C C C C" " " r8Nr:)rkrur5rrfr_vision_pos_embedsr)r3r rrrs r7ruz/SAM2DynamicInteractivePredictor.get_im_featuress 0B/Q/QRVX[cgct/Q/u/u, =*" " #&y"~z#2##G#G" " "  &!.$r8torch.Tensor | Nonec ddtj|jd|jddz|jddzfd|j|jtj|j|jjfd|j|jtj|jdfd|j|jd}t|D]P\}}||jksJ| t|}|j |||g||g} } |||gdnd} | | Jd | || | | } | | d } | jd d|d jd dks4Jd |d jd dd | jd dd|d| |d ||dz<| d|d||dz<d| vr| d|d||dz<Rt!j|d |j|jjdk|jdd}|jjr|j|}|j|j|j||dd\}}||d<||d<|j|dS)a Append the imgState to the memory_bank and update the memory for the model. Args: obj_ids (list[int]): List of object IDs corresponding to the prompts. points (torch.Tensor | None): Tensor of shape (B, N, 2) representing the input points for N objects. labels (torch.Tensor | None): Tensor of shape (B, N) representing the labels for the input points. masks (torch.Tensor | None): Optional tensor of shape (N, H, W) representing the input masks for N objects. Nrrrrirz'Either bbox, points or mask is requiredrrzExpected mask shape z but got z for object rrrr)rrFrT)rrrrrrr)r>rr5rarrGrJrrrrJr4addrrwrrrrFrrrrr_rr3r)r3rrergrfrrkrzrpointlabelr/robj_maskrrrs r7r:z-SAM2DynamicInteractivePredictor.update_memorys"!%#*'DJqMQ,> 1 QR@RS"&{  z')>?"&{  $):'+&{ $$$  2#7++ p pIAvD-----))#f++66G    ) ) )!1#;s 5E','85!:d##dD$(8(8:c(8(88//'5%>>C|,~bcc*.>|.L.RSUSVSV.WWWWK+;L+I+OPRPSPS+TKK_g_mnpnqnq_rKKAHKKKXWWIQ .w1/DEEH^ +Ggk,AB(CHHJJ66UXYnUo$%:;GgPQkZZN,0J,I,I!%!2 . 01F G! -J- - )/0@+,.=*+  011111r8r int | None torch.Tensorct|jdkst|tr|jd|jjz}nP|\}}|j|jdd|j dd||d}| dddj |j |jjj g|jdRS)aPrepare memory-conditioned features for the current image state. If ``obj_idx`` is provided, features are prepared for a specific prompted object in the image. If ``obj_idx`` is None, features are prepared for all objects. If no memory is available, a no-memory embedding is added to the current vision features. Otherwise, memory from previous frames is used to condition the current vision features via a transformer attention mechanism. Args: obj_idx (int | None): The index of the object for which to prepare the features. Returns: pix_feat_with_mem (torch.Tensor): The memory-conditioned pixel features. rr:N)currcurr_posmemory memory_posnum_obj_ptr_tokensrr<)r`r3r=rJr_rJr\get_maskmem_encmemory_attentionr@rSrr5d_modelr)r3rpix_feat_with_memrMmemory_pos_embeds r7$_prepare_memory_conditioned_featureszDSAM2DynamicInteractivePredictor._prepare_memory_conditioned_featuress t A % %GS)A)A %!% 1" 5 8O O  (,';';'='= $F$ $ ; ;&rss+/4+#$ !<!! 7 ((Aq116   J ' / _R     r8cgg}}|jD]}||ddddd|dddddd}||jj|jjdz z}||tj|d}tj|d}||fS)zfGet memory and positional encoding from memory, which is used to condition the current image features.rr<rrrr:r) r3rrrSrJmaskmem_tpos_encrr>r)r3 to_cat_memoryto_cat_memory_pos_embedr maskmem_encrMrTs r7rPz/SAM2DynamicInteractivePredictor.get_maskmem_enc@s13R. $ 0 8 8   !12D!E!M!Ma!P!P!X!XYZ\]_`!a!a b b b*+<=bAII!LLTTUVXY[\]]K% (CDJDZ]^D^(__K # * *; 7 7 7 7=a000 9%SAM2DynamicInteractivePredictor.track_step..s-"i"i"i$4(D*;*A!*D(D#E"i"i"ir8)backbone_featuresrrrqrf)rrrr) rJ$use_mask_input_as_output_without_samr_rSrrQrRr_use_mask_as_outputrU_forward_sam_headsrf) r3rrErFr/pix_featr^ low_res_masksrrrrSs @r7rz*SAM2DynamicInteractivePredictor.track_stepXsD4   O (,44Q1==H$x}R)D)Lct_aObcccHSWS]SqSqrvSwSw PAq!]NG=P=P!% I I' R R 9@9L 1"1" 5 5Rc SWS]SpSp"3OVObeUKKKhl !&"i"i"i"iRVRh"i"i"i TqTT PAq!]NG=P(#1#6    r8) r4rr5rr/rJr r!r0r1r7)r.r6rdr7rfr8rer7rgr9rr9r:r;r0r<)r r6r0r1r5) rr9rerArgrArfrAr0r1)rrHr0rI)r0r<)rzrJr0rH) rrHrErArFrAr/rAr0r\)r8r9r:r;r r+rrsrur:rUrPrrr=r>s@r7r.r.@sc6+/"& (((((((>,026+/#'$(#@C@C@C@C@CD%%%% %)&*&*%) N2N2N2N2N2`! ! ! ! F ( ( ( ( 4 4 4 4#%)%)$( 1 1 1 1 1 1 1 1 1 r8r.c6eZdZdZgdZdZdfd ZdZxZS) SAM3PredictorzSSegment Anything Model 3 (SAM3) Interactive Predictor for image segmentation tasks.) rh)ri)HrjNTclt||tjgdddd|j|_tjgdddd|j|_dS)zTSetup the SAM3 model with appropriate mean and standard deviation for preprocessing.)_@rmrmr:rN) r*rr>rrrFrGrHrI)r3rJrr6s r7rzSAM3Predictor.setup_models E7+++L!6!6!677<s@r7rfrfsw]]N FVVVVVVRRRRRRRr8rfceZdZdZdZedZdZddZddd Z d Z ddd Z e ddd Z d Z ddZdS)SAM3SemanticPredictorzGSegment Anything Model 3 (SAM3) Predictor for image segmentation tasks.cPddlm}||jj|jjS)ror)build_sam3_image_modelrr)rtryr,rJrs)r3rys r7rzSAM3SemanticPredictor.get_modelrur8c@|jj|S)z2Extract image features using the model's backbone.)rJbackbonerYrs r7ruz%SAM3SemanticPredictor.get_im_featuressz"00444r8ct|dks Jdt|jdddfd|DS)rQrrRFT)rSrT scale_fillc(g|]}|SrVrXrYs r7r]z7SAM3SemanticPredictor.pre_transform..r^r8r_rbs @r7rBz#SAM3SemanticPredictor.pre_transformsQ,2ww!|||U|||djuUtTTT ////B////r8Nc|=tj||j|j}|jdkr|dn|}t j|}|dddddfxx|dzcc<|dddddfxx|dzcc<|!tj|j dd}tj|tj |j}|j d|j dks'Jd|j dd |j dd | ddd }| dd}||fS) zRPrepare prompts by normalizing bounding boxes and points to the destination shape.Nrrrr<r:rrrrrC) r>rrrGrr xyxy2xywhr@rrwrr)r3rrdrgs r7_prepare_geometric_promptsz0SAM3SemanticPredictor._prepare_geometric_promptssV  _V43CDKXXXF%+[A%5%5VD\\6F]6**F 111add7OOOy| +OOO 111add7OOOy| +OOO~crc!233_V5;t{SSSF<#v|B'7777hFL$4hhU[UabdUehhh877[[Q**F[[Q''Fv~r8textlist[str] | Nonec|dn)|t|nt|jj}||}|Ht t|D]&}|||g||g'|dg}|+|jj|kr|j||j|tj ||j tj |}|S)zPRun inference on the extracted features with optional bounding boxes and labels.Nrvisualrr'r]text_idsgeometric_prompt) r`rJr_get_dummy_promptr append_boxes set_classesforward_groundingr>rrGlong) r3r/rdrgrncrrkoutputss r7rxz)SAM3SemanticPredictor._inference_featuress $QQt7G#d)))SQUQ[QaMbMb11"55  3v;;'' H H --faSk61#;GGGG| z   0D 8 8 J " " " - - -*..!\"T[ KKK-/   r8c :ddl}|d}|d}|d}|}|dd} || zd}t jt t|jd|j |j dddf |} t j ||d | d gd }||j jk} || || }}tj|dddd f|dddd f<|ddd df|j jrdndz} |dddd f| z} |j| |ddd f|j j} || || }}t)|jddt|jdD}t-|t stj|}g}t1|g|g||jdD]\}}}}|jddkrdt jd|j }}nt7j|d|jdddddk}|dddgfxx|jdzcc<|dddgfxx|jdzcc<|t?||||||S)NPost-process the predictions to apply non-overlapping constraints if required.rN pred_boxes pred_logitsrpresence_logit_decrr:r.NrrCrrc,g|]}t|SrXrrjs r7r]z5SAM3SemanticPredictor.postprocess.. s-Z-Z-Zc!ff-Z-Z-Zr8rrr<rr%rO.r;r) rsigmoidr+rr>rrrrwrrG expand_asrr,r r xywh2xyxy agnostic_nmsrrrrJr=rrr'rrrrMrr )r3r r r rrrrrpresence_scorepred_clsrc nms_boxesrr rfr{rrs r7rz!SAM3SemanticPredictor.postprocesssU<( M* <( !))++ 34<<>>HHKK"^3< NN!+D!1:d3CJ  G-Z-ZeKDUVWDX>Y>Y-Z-Z-Z[[)T** A5i@@I03ZL:,PY[_[efg[h0i0i d d ,E5(H{1~""#U[ @Q%R%R%Ru ekkmmD&98>"1";MT^___`abehhcAq6k"""hnQ&77"""cAq6k"""hnQ&77""" NN78(%u\abbb c c c cr8cv|jd|}|jd|}|jd|}|j||n|j}||jddjdd||}|j|g|Rd|iS)z:Perform inference on a single image with optional prompts.rdrgrNrrr<)r0rmr/rurr'rwrx) r3r.rdrgrr,rrr/r0s r7rszSAM3SemanticPredictor.inference s!!(F33!!(F33|--/3}/D4''+++$-11$*Q-2B2H!2LfV\]]'t'F7FFFFFFr8cfddl}||dd||}|j|g|Rd|i}|d} |d} |d} | } |dd } | | zd } t jtt| j d| j | j dddf | }t j| | d |d gd } | |jjk}| || |} } t#j| ddddf| ddddf<| ddddf|jjrdndz}| ddddf|z}|j|| dddf|jj}| || |} } | j ddkrdt jd| j } } nt/j| d|dddddk} | dxx|d zcc<| dxx|dzcc<| dxx|d zcc<| dxx|dzcc<| | fS)aMPerform prompts preprocessing and inference on provided image features using the SAM model. Args: features (dict[str, Any]): Extracted image features from the SAM3 model image encoder. src_shape (tuple[int, int]): The source shape (height, width) of the input image. bboxes (np.ndarray | list[list[float]] | None): Bounding boxes in xyxy format with shape (N, 4). pixels. labels (np.ndarray | list[int] | None): Point prompt labels with shape (N, ). text (list[str] | None): List of text prompts corresponding to the classes. Returns: pred_masks (torch.Tensor): The output masks in shape (C, H, W), where C is the number of generated masks. pred_bboxes (torch.Tensor): Bounding boxes for each mask with shape (N, 6), where N is the number of boxes. Each box is in xyxy format with additional columns for score and class. Notes: - The input features is a torch.Tensor of shape (B, C, H, W) if performing on SAM, or a dict[str, Any] if performing on SAM2. rNr<rrrrrrr:rrrrCrrrrrrr%rO).r).r).r<).r;)rrrxrr+rr>rrrrwrrGrrr,r r rrrrrrrrM)r3r/rrdrgrrr0r rrrrrrrrrs r7r3z(SAM3SemanticPredictor.inference_features$ s4 11)BQB-PP((GGGGG$GG<( M* <( !))++ 34<<>>HHKK"^3< NN!+D!1:d3CJ  A ! # #%)5;vjFW+X+X+X JJz'7'7'9'9$'?2A2U_```abcfiiJ v   )A, .    v   )A, .    v   )A, .    v   )A, .   :%%r8c,i|_i|j_dS)z$Reset the prompts for the predictor.N)r0rJtext_embeddingsr!s r7 reset_promptsz#SAM3SemanticPredictor.reset_promptsb s %' """r8rc ttjd|d|jtj|d|jtj}|S)z-Get a dummy geometric prompt with zero boxes.rrCrr')box_embeddingsbox_mask)rr>rrGr;)r3 num_promptsrs r7rz'SAM3SemanticPredictor._get_dummy_promptg sQ! ;q+qMMM[a 5:VVV    r8r+NNNrrr*)r8r9r:r;rrrurBrrxrrsr3rrrXr8r7rwrwsQQRRR 5550004(&&&&PGGGGG !% ;&;&;&;&;&z(((       r8rwceZdZdZdZdS)SAM3VideoPredictorzMSegment Anything Model 3 (SAM3) Video Predictor for video segmentation tasks.c |}|d}|d}|d}t|d}t|ddkrtd||dvr:d}|||} |jr"|js|dkr||n]||d vrd }|||} nBd }||||d d d d d | } | |||<|||||| |||d|| d dd} | d} || | fS)aPerform image segmentation inference based on the given input cues, using the currently loaded image. This method leverages SAM's (Segment Anything Model) architecture consisting of image encoder, prompt encoder, and mask decoder for real-time and promptable segmentation tasks. Args: inference_state (dict): The current state of inference, including input cues and previous outputs. frame_idx (int): The index of the current frame in the video sequence. rxrr|r}ryrr~rrFNT) rxr{rrrrrrrnrrrr) r`rrprqrrrrrr) r3rnr{rrxrr|rrrr obj_scoress r7propagate_in_videoz%SAM3VideoPredictor.propagate_in_videos s%m4 !),"12K"L9:: {/0 1 1Q 6 6PQQ Q +,@A A A.K%k259K3 =9^ =blpqbqbq55e<<< -.FG G G2K%k259KK2K::'%#(!  $ /;  K/:K $U +  ' ' ' O O O ##E; Ud#eee0188??? .66q!<<  !67  J..r8N)r8r9r:r;rrXr8r7rrp s)WW0/0/0/0/0/r8rceZdZdZdZdZdZdZdZdZ dZ gdZ d Z e d d d d d d d d dddddd ddddddddd d fdofd Zdpfd ZfdZedZdqdrdZdZdsdZe dtdZdud Z dvdwd0Zedxd2Zdyd3Zd4Zd5Zdzd7Zd{d>Zd|d@Z d}d~dDZ!ddFZ"e dddIZ# dddNZ$ddPZ%ddVZ&dd]Z'dd`Z(ddcZ)dddZ*deZ+ddhZ,dvddlZ-dmZ.ednZ/xZ0S)SAM3VideoSemanticPredictorz9Segment Anything Model 3 (SAM3) Video Semantic Predictor.皙?$r:irr<rgrkNrOrr;TFrr r!cTt|||||_||_||_||_||_| dkr| | ksJ| | ksJ| |_| |_| |_ | |_ | |_ ||_ ||_ ||_||_||_||_d|_d}d}||_||_||_||_||_||_||_t5||_i|_|jd|jdS)zTInitialize the SAM3VideoSemanticPredictor with configuration and optional overrides.rNi'r)r5rm) r*r+score_threshold_detectiondet_nms_threshassoc_iou_threshtrk_assoc_iou_threshnew_det_threshhotstart_delayhotstart_unmatch_threshhotstart_dup_threshinit_trk_keep_alivemax_trk_keep_alivemin_trk_keep_alive8suppress_overlapping_based_on_recent_occlusion_thresholdsuppress_det_close_to_boundary*decrease_trk_keep_alive_for_empty_maskletso2o_matching_masklets_enablefill_hole_area _dist_pg_cpumax_num_objectsnum_obj_for_compilerecondition_every_nth_framemasklet_confirmation_enable+masklet_confirmation_consecutive_det_threshreconstruction_bbox_iou_threshreconstruction_bbox_det_scorertrackerrnrrrrs)r3r4r5r rrrrrrrrrrrrrrrrrrrrrrrr6s r7r+z#SAM3VideoSemanticPredictor.__init__ sb\ i444)B&, 0$8!, A  *n<<<<&.8888,'>$#6 #6 "4"4 D E/M+:d7,H),  .#6 +F(+F(;f8.L+-J**I>>> ! )*11$/BBBBBr8ct||ddlm}||jjd}|j|ddS)z+Setup the SAM3VideoSemanticPredictor model.rrpF) with_backbone)rJrN)r*rrtrqr,rJr)r3rJrrqr6s r7rz&SAM3VideoSemanticPredictor.setup_model sk E7+++666666'&tyeLLL   ue <<<<.. s,'W'W'WqAq,A(B(B'W'W'Wr8rLrMs @r7r]z;SAM3VideoSemanticPredictor.setup_source.. s5&s&s&s\]'W'W'W'W'WDJ'W'W'W&s&s&sr8rNN) r*rrarrJrrPmemory_encodermask_downsampler interpol_sizers` r7rz'SAM3VideoSemanticPredictor.setup_source s~ V$$$!Z  $$TZ000&s&s&s&sar&s&s&s #!\/>O]r8ct|jdkrdS|jJ|jjdksJ|jj}|giddg|zd}||_dS)aInitialize an inference state for the predictor. This function sets up the initial state required for performing inference on video data. It includes initializing various dictionaries and ordered dictionaries that will store inputs, outputs, and other metadata relevant to the tracking process. Args: predictor (SAM3VideoSemanticPredictor): The predictor object for which to initialize the state. rNr)rtracker_inference_statestracker_metadata text_promptper_frame_geometric_prompt)r`rnrr&r)rrrns r7rsz%SAM3VideoSemanticPredictor.init_state! s y( ) )A - - F ,,, %0000&- $(* "+/&:*=   %4 !!!r8rrc|jjdz }||jd<d|jvr|||||||dS)z.M s#V#V#VvN6$:#V#V#Vr8rr<rr%rOrc,g|]}d|S)obj_id_to_scorerXrZrzr s r7r]z:SAM3VideoSemanticPredictor.postprocess..Q s$MMMf()&1MMMr8c,g|]}d|S) obj_id_to_clsrXrs r7r]z:SAM3VideoSemanticPredictor.postprocess..T s#KKKF'/KKKr8rrr:c34K|]}t|VdSrirrjs r7rlz9SAM3VideoSemanticPredictor.postprocess..] s(/h/h1A/h/h/h/h/h/hr8rrcDg|]}|dvrd|ndS)obj_id_to_tracker_scorerrXrs r7r]z:SAM3VideoSemanticPredictor.postprocess..` sO # &/H)III"";rrGrrrrMrwrrr,r anyrr)rrrJmax._apply_object_wise_non_overlapping_constraintsr+rrr'rr )r3r r r  curr_obj_idsrrrpred_idsrrrtracker_scoresr rfr{rrrs ` @r7rz&SAM3VideoSemanticPredictor.postprocessB s/0n113344 )T** A5i@@I$(J$4$@$@   b |   ! !%)5;vdk+R+R+R JJ#V#V#V#V#V#V#V\]^^^Jz'7'7'9'9$'?1ASTVUVTVAW^hiiijklorrJ|L JL]^^^H,MMMM MMMV`VgK|KKKKlKKKT^TeH $).0JNNvN4N4NND#D)J,Z88JXd^AAAtG4k$6G 6RT\]aTbclTmntvJ" jii/h/hjQRQRQRTUQUFVFZFZF\F\F`F`FbFbefFf@g@g/h/h/h&h&h!i!i"Q&&!& '3 &, " " " "GG",,Q//&0033)*Hgajj  03ZL:,PY[_[efg[h0i0i d d ,E5(H NN78(%u\abbb c c c cr8c J|p|j}|d}|ddu}|d|du}|||d||d|d|s)|t|dn |d|||d |p| \}}} } } } } | |d<| |d <||| | d |d }| d }|d}||d<| |d<|jr=|dd}||jk}| d||d<ng|d<|S)zBPerform inference on a single frame and get its inference results.rrNrrr.rrr) r{rrr.rrtracker_states_localtracker_metadata_prevallow_new_detections"obj_id_to_tracker_score_frame_wise)rrrrmetadataremoved_obj_ids frame_statsmasklet_confirmationstatusobj_ids_all_gpuunconfirmed_obj_ids)rn_det_track_one_framerr`r UNCONFIRMEDtolist)r3r{rrnrhas_text_prompthas_geometric_promptrrrtracker_states_local_newtracker_metadata_newrr^rrrris_unconfirmeds r7rz6SAM3VideoSemanticPredictor._run_single_frame_inferencew s)AT-A./IJ)-8D./KLYW_cc  % %&|4t$$Z0,N&&3z7R3S3S&TTT$%AB9M!5"12D"E!0!H4H &      $  "7O23.B*+-.*';<`'abk'l   ( 3"#45!0 (M  + ,45h?F#t'77N)=>O)PQ_)`)g)g)i)iC% & &)+C% & r8c|p|j}| | Jd|du}|r|nd}t|tr|gn|}|r|nd|d<t|}t j||jtj} | |d<|+|jj |kr|j || |j dd j dd ||\}}|du|duksJ|| } |Ctt|D]&} | || g|| g'| |d |<||d |} || fS)aBAdd text, point or box prompts on a single frame. This method returns the inference outputs only on the prompted frame. Note that text prompts are NOT associated with a particular frame (i.e. they apply to all frames). However, we only run inference on the frame specified in `frame_idx`. Nz:at least one type of prompt (text, boxes) must be providedrrr'rrrrr<rrF)rrn)rnr=rr`r>rrGrrJrrrr'rwrrrr) r3r{rrdrgrnuse_text text_batchnrrrkrs r7rz%SAM3VideoSemanticPredictor.add_prompt s*AT-A6#5#57s#5#55t#-ttX)$44>dVV$ 19)Ct &  OO<$+UZHHH&. #   0D 8 8 J " " " - - -88Aq9I9OPRQRPR9SU[]cddd"d(:;;;;11a1@@  3v;;'' H H --faSk61#;GGGGCS45i@..y%Yh.ii#~r8ctj|dk|d|}|jj|}tj|dk|tj||}|S)zhApplies non-overlapping constraints object wise (i.e. only one object can claim the overlapping region).r).NN)r)r>whererrJrclamp)r3rrrpred_masks_single_score!pixel_level_non_overlapping_maskss r7rzISAM3VideoSemanticPredictor._apply_object_wise_non_overlapping_constraints sy#(+j1nj>Y[k"l"l,0L,>,a,a #- - )[ - 1  K (8 9 9 9  r8r.rIrr{rJrrr;rrr list[Any]rr\rc ||||| } |ikr'||||||\} } |||| | | ||\} }| dt }|||| || }|| | || |}|d}|d}tj |d | d d }| j d d krj| } |d }|d|tt|| ||||||| fS)aThis function handles one-step inference for the DenseTracking model in an SPMD manner. At a high-level, all GPUs execute the same function calls as if it's done on a single GPU, while under the hood, some function calls involve distributed computation based on sharded SAM2 states. - `input_batch` contains image and other inputs on the entire video; it should be identical across GPUs - `tracker_states_local` holds the local masklet information in this GPU shard - `tracker_metadata_prev` manages the metadata for SAM2 objects, such as which masklet is hold on which GPUs it contains both global and local masklet information )r.rrr)r{rr)r{rdet_outtracker_low_res_masks_globaltracker_obj_scores_globalrrreconditioned_obj_ids)r{rrrtracker_update_plan)rrrrrrrnum_objnum_obj_dropped_due_to_limit)num_obj_trackednum_obj_droppedrrr)run_backbone_and_detectionr(_initialize_metadatarun_tracker_propagation!run_tracker_update_planning_phaserr"run_tracker_update_execution_phase build_outputsr@rrwrrr)r)r3r.rr{rrrrrrrrrrr rrrrrrtracker_obj_idss r7rz/SAM3VideoSemanticPredictor._det_track_one_frame s.11-!5 2   !B & & ! ( ()B)B)D)D E E EBFB^B^!5"7C_C C ?$&?594Z4Z)E&?"7!55[5 5 11!4 7 78OQTQVQV W W$(#J#J!!5 3 $K$ $  ++)E"7 3"7 ,  //@A,_=  "v&:9&EFF23QR  % *1 - 1 1(A(I(I(K(K(R(R(T(T %3I>O !E Fy Q X XS*CDDEE       $  %  r8皙?c|d\}}}}||zdz }||zdz }||k|d|z kz||kz|d|z kz}|S)zSuppress detections too close to image edges (for normalized boxes). boxes: (N, 4) in xyxy format, normalized [0,1] margin: fraction of image r:r<r)unbind) r{marginx_miny_minx_maxy_maxx_cy_crs r7&_suppress_detections_close_to_boundaryzASAM3VideoSemanticPredictor._suppress_detections_close_to_boundaryP sn&+\\"%5%5"ueUu}!u}!f sV|!34f EsU[|I[\ r8c||}|j|||}|||}|||S)z.Run backbone and detection for a single frame.r)rurJr_extract_detection_outputs_cache_backbone_features)r3r.rrrr/sam3_image_outrs r7rz5SAM3VideoSemanticPredictor.run_backbone_and_detection^ sl''++55!HGW6  11.BVWW %%n555r8c|dd}|s|dz }tjt t |jd|j|jdddf |}|d}|d}||j k}||||||||d S) z%Extract and filter detection outputs.rr:gחArrNpred_boxes_xyxyr)bboxr/rr) rrr>rrrrwrrGrr)r3r4r pred_probsrr6rrs r7r2z5SAM3VideoSemanticPredictor._extract_detection_outputsj s#M2::2>>FFHH # *#c)J< z'*++ , ,"$    !!T' 9Z((  )):;#L1 D::#D)t$ &D>    r8c|jjj}|dd}||dd||dd|ddg}|d|d|d }||j_d S) z'Build and cache SAM2 backbone features.r]sam2_backbone_out backbone_fpnrrr<r:vision_pos_enc)vision_featuresr<r;N)rrJrhconv_s0conv_s1r])r3r4rhr`tracker_backbone_fpntracker_backbone_outs r7r3z3SAM3VideoSemanticPredictor._cache_backbone_features s<->~./BC  $ $U>%:1%= > >  $ $U>%:1%= > > . !! $  4B7#$450   %9 !!!r8dict[str, np.ndarray]c|||\}}}tj||dks$Jd||d|}|}||fS)zGRun the tracker propagation phase for a single frame in an SPMD manner.r{rz{} != {})&_propogate_tracker_one_frame_local_gpur@rnr) r3r{rr obj_ids_locallow_res_masks_localobj_scores_locallow_res_masks_globalobj_scores_globals r7r!z2SAM3VideoSemanticPredictor.run_tracker_propagation s@D?j?j I@k@ @ < *,<vm'|S)z=Recondition masklets based on new high-confidence detections.r/rrFrrrrzAdding new mask for track at frame z . Objects z are all reconditioned.rnr{rzrf)rrrr+rrr@ritemrr debugrrrDr)r3r{rrLrrr trk_obj_iddet_idxnew_masknew_mask_binaryHIGH_CONF_THRESHreconditioned_states_idxr obj_score state_idxrnrs r7_recondition_maskletsz0SAM3VideoSemanticPredictor._recondition_masklets s$C#H#H#J#J U U Jvw1'<=H h0033$:LS]mrsssvww  # '*uu $h/ :jHII!LQQSSG1':I.78L.M.M < <* ?/)"<<<"$444LTZTT9TT`opy`zTTTL00(7"+)- 1 -00;;;/ U U 99:Ns:STTTT U##r8rc t|dt|dt|dt|dt|dit|dd}t} |d} |d } |d } |d } || | ||d \}}}}}|jrG|| |}||}tj |d}t|}d }||z|j krltj d|j d|d||j |z }||z }||| |}t||ksJt|}|ddztj|z}t|d}t!|dr|jr ||||||||\}}nt}||d<|||||||| d}d}|jd krt|d kr|D]\}}|d |}|d |} t+|d|}!n#t.$rYXwxYw||!}"|"d k}#|# }$|$d krt3|#d d }%|"jdd\}&}'t;j|%d |'z |%d|&z |%d|'z |%d|&z g|%j}(|d })|(d }*tA|)|*d }+|+|jkr"| |j!krd}| "||j#d ko ||j#zd kot|d k},|,s|r|$|||||||%d }-|-d krTt!|dr|jr%|j&dkr|'||||||}|(||||d}.t|d krtj)|.|g}.t|d kr+tj*|.t+|}/|.|/}.|.|d<t|.|d<t|d kr|d+tY|| ||d+tY|| ||d|+tY|| |t[|dtj-||d<|D]:}0d|d|0<d|d||0<|d .|0d;d|vsJ|j/r1|0|d|d|d||!}1|1|d<||fS)"zKRun the tracker update planning phase for a single frame in an SPMD manner.rrrrr max_obj_id)rrrrrobj_id_to_last_occludedr]r/rrr7) det_masks det_scores_np trk_masks trk_obj_idsrzhitting self.max_num_objects=z with new_det_num=z and prev_obj_num=rr_warm_up_complete)r{rdet_to_matched_trk_obj_idsnew_det_obj_idsempty_trk_obj_idsunmatched_trk_obj_idsr)new_det_fa_indsrergrdobj_ids_newly_removedrrLrFrNr<r;rTr)rdgr^)robj_ids_all_gpu_prevobj_ids_all_gpu_updatedrdre)1rrrMr(r)_associate_det_trkrr0r@rr`rr warning_drop_new_det_with_obj_limitrhasattrrc_process_hotstartrrrindex ValueErrorrQrr+rrwr>rrGrrrDrr[rr/_suppress_overlapping_based_on_recent_occlusion_tracker_update_memories concatenateisinr(rrrmr"update_masklet_confirmation_status)2r3r{rrrrrrr rdet_mask_predsr` det_cls_np det_bbox_xyxyrhrgrdrLrfr prev_obj_num new_det_numrnew_det_num_to_keepre metadata_newrirshould_recondition_iourSrTdet_box det_scoretrk_idx tracker_mask mask_binary mask_areatracker_box_pixels mask_height mask_widthtracker_box_normalized det_box_batchtracker_box_batchrshould_recondition_periodicrupdated_obj_ids_this_gpu is_removedrzrs2 r7r"zO(PQQ%&;O&LMM2:;PQu;v2w2w')"#8#FGG  !$(/v$+H$5$;$;$=$=$A$A$C$C$I$I$K$K !(!5!5!7!7!;!;!=!=!C!C!E!E &-fo   # #$'2-i8 $    ! & +   . B>>}_?]^^D-dhhjj.>.>.@.@AOv3I>?? /** '($ + %(< < < Ned&:ee;eeVbee f f f"&"6"E +69L+L ("??Q^`sttO''+>>>>>o..K0 =ABIkDZDZZ  5j ABB t011 *T5K *262H2H#+E /"3&;%3I33 / !<<%(EE !+7Z( /.%:*D%:,H/N%:   "'  . 2 2s;Z7[7[^_7_7_'F'L'L'N'N$ :$ :# G!&/'2#H-g6 "#8#CDDJJ:VVGG!H >&99N9Nq9Q9Q%R%R%Z%Z[\%]%]"*6*??B<<<dNpApAp-1*)--j999  ,q 0 9D<<A 9344q8 $ ' *@   & &/$%)    266q99 >>4!455 9O PSVVV373g3g!4-,- 440  ) )*> Yu ) v v v$8 #B   ! # #')~7OQ`6a'b'b $ $ % % ) )!94@U;V;VWWJ'? 'L $*BY'*-.F*G*GY'   ! # # !2 3 : :3P]^mPn;o;o p p p  1 8 8_jYhNi9j9j k k k !E Fy Q X XO]?%CDD   255I,5WY[Y_`oYpYp1q1q  ., N NF>B !2 3F ;\` !E Fy QRX Y !: ; ? ? M M M M11111  + 8>>-j9%:9%E(rNEVER_OCCLUDEDALWAYS_OCCLUDEDrGr)rZrz#binary_tracker_low_res_masks_globalrir3rs r7r]z^SAM3VideoSemanticPredictor._suppress_overlapping_based_on_recent_occlusion.. s   **CDHH  7=EZ7Z7Z 3 3`d`t#F#M"'*      r8rr:rc2i|]\}}|||dzSr*rX)rZrrzlast_occluded_news r7 z^SAM3VideoSemanticPredictor._suppress_overlapping_based_on_recent_occlusion.. s;???ETWf)'GaK*?@???r8r^) rr`r>r8_get_objects_to_suppress_based_on_most_recently_occludedrrr NO_OBJ_LOGIT)r3r{rrr rirobj_ids_globalrlast_occluded_prev to_suppressis_obj_occludedis_obj_occluded_or_suppressedrrs` ` ` @@r7rszJSAM3VideoSemanticPredictor._suppress_overlapping_based_on_recent_occlusion s./y9.JQ.N+166q99 >>~&&*444W#n2E2EWW:WW544"'       #1   """ "WW3" K!D G GH G U UVO,;k,I ) 2 8 8 : : ?H ; <????XabpXqXq??? !: ; 9=8I ( 5++r8rc.|d}|d}|}|} |d} t| dkrrE_tracker_add_new_objects_tracker_remove_objects) r3r{rrrrrhrenew_det_obj_ids_localnew_det_fa_inds_localrinew_det_fa_inds_local_t new_det_maskss r7r#z=SAM3VideoSemanticPredictor.run_tracker_update_execution_phase s'::K&L&9:K&L,;,;*=>U*V $ % % ) )&+&67L&M&M #*1&/:Q*RM#'#@#@#%1+%9 $A$$  $ % % ) )  ( ()=?T U U U##r8r set | Nonec|d}|d}i}|d}|d} t|t| ksJt|| D] \} } | || < tj|} |d| d} t|t| ksJt|| D] \} } | || < |it|dkrV|di}|D]=} || }|$|d|d|| <>|S) z-Build the output masks for the current frame.rhrerrr/NrrL)r+r`rr>rEr)rrrrrrhrerexisting_masklet_obj_idsexisting_masklet_binaryrzr/new_det_fa_inds_tnew_det_low_res_masksrLrTs r7r$z(SAM3VideoSemanticPredictor.build_outputs s'::K&L&9:K&L$9#C ">"H"H"K"K+,,4K0L0LLLLL 8:QRR * *LFD%)N6 " "",_== '0A B L LQ O O?##s+@'A'AAAAA1FGG * *LFD%)N6 " " ! ,5J1K1Ka1O1O.A.E.EFgik.l.l +/ S S9==fEE&-4V_W-E-O-OPQ-R-RN6*r8binary_low_res_masks last_occludedrrHc|jtjksJd|jtj|d|jtj}t |dkr|St|d|d}||j k}tj |d} | d} | d} |s tj n tj } | | | | z| dkz} | | | | z| dkz}| d|dz}tjdrT|Q| } |}|}| jd}t)|D]Y}t)|D]G}| ||fr;tjd |d ||d ||d ||d || HZt)|D]Y}t)|D]G}|||fr;tjd |d ||d ||d ||d || HZ|S) NzExpected boolean tensor, got rr'r)diagonalr:r z frame_idx=z: Suppressing obj z last occluded z in favor of )rr>r;rrrGr`rrrtriur+gtltrr isEnabledForr(r)rwrrR)r3rrrr{rrrmask_iou_threshoverlapping_pairslast_occ_expanded_ilast_occ_expanded_jcmp_opsuppress_i_masksuppress_j_maskrrkjs r7rzSSAM3VideoSemanticPredictor._get_objects_to_suppress_based_on_most_recently_occluded7 s$)UZ7779uYmYs9u9u777k % %a ( ('.*   w<<1   +33A668L8T8TUV8W8WXX!^^!JCCC+55a88+55a88!(6eh f(*=>> ?"R' )  f(*=>> ?"R' )  &))a)00?3F3F13F3M3MM   r " " y'<-113399;;O-113399;;O)--//5577M).q1J:&&  z**A&q!t, eyeeWQZeeXefgXheew~@AxBeeS`abScee:&&  z**A&q!t, eyeeWQZeeXefgXheew~@AxBeeS`abScee r8inference_statescg}g}g}|D]}t|ddkr|j||\}}} t|tsJ||||d|| dt|dkrBtj |d} tj |d} | d} n@tj dg|j dRd|j i} tj d|j } || | fS)zaInference_states: list of inference states, each state corresponds to a different set of objects.rrrDrrrGr) r`rrr=rextendrrr>rrrPrG) r3rr{rFlow_res_masks_listobj_scores_listrn out_obj_idsout_low_res_masksout_obj_scoresrGrHs r7rEzASAM3VideoSemanticPredictor._propogate_tracker_one_frame_local_gpu{ s / > >O?9-..!33=A\=\=\9>]>> :K*Nk400 0 00   - - -  % %&7&?&?&B&B C C C  " ">#9#9!#<#< = = = = ! " "Q & &"'),>A"F"F"F $ya@@@ "5"="=a"@"@  "'+a"]$2Ea2H"]"]"]QUQ\"]"] ${1T[AAA 13CCCr8r_r` np.ndarrayrarbc  |j}|j}|j}|s Jd|s Jd|dt |ks0Jd|ddt ||ddkrpt j|d}t jgt j } t jgt j } i} i} || | | | fS|ddkryt jgt j }|dk d } || } || } i} i} || | | | fS|j dd|j ddkrt j|j ddt j|j ddkrLtj|d |j ddd d d }nKtj|d |j ddd d d }|dk}|dk}t'|d |d }| }|jrqdd lm}d |z }||\}}t j|dt4}t7||D]\}}|||f|krd||<n||k d}| d } t j| |}||} || } t j||kt jt j ||kd }t j|d}i} i} t j|d }||j k|z}t j!|d |j"k}tGt j||zd}tI|dD]D}|||ddf|k| |<||vr%|||%}|| |<E|| | | | fS)ajMatch detections on the current frame with the existing masklets. Args: det_masks: (N, H, W) tensor of predicted masks det_scores_np: (N,) array of detection scores trk_masks: (M, H, W) tensor of track masks trk_obj_ids: (M,) array of object IDs corresponding to trk_masks Returns: new_det_fa_inds: array of new object indices. unmatched_trk_obj_ids: array of existing masklet object IDs that are not matched to any detections on this frame (for unmatched, we only count masklets with >0 area) det_to_matched_trk_obj_ids: dict[int, np.ndarray]: mapping from detector's detection indices to the list of matched tracklet object IDs empty_trk_obj_ids: array of existing masklet object IDs with zero area in SAM2 prediction z'float tensor expected (do not binarize)rz7trk_masks and trk_obj_ids should have the same length, rrrrNrrFr)linear_sum_assignmentrTr)&rrris_floating_pointrr`r@rrint64rr(r)rwprodrrr+rrrrMrscipy.optimizerrr;r logical_and logical_notnonzeroargmaxrWrHIGH_IOU_THRESHrrrQ) r3r_r`rarb iou_thresholdiou_threshold_trkrrhrgrfrdrLtrk_is_nonemptydet_masks_binarytrk_masks_binaryiousious_npr cost_matrixrow_indcol_indtrk_is_matcheddrtrk_is_unmatched is_new_detdet_to_max_iou_trk_idxdet_is_high_confdet_is_high_ioudet_is_high_conf_and_iourSs r7rlz-SAM3VideoSemanticPredictor._associate_det_trk s.-  5,**,,WW.WWW,**,,WW.WWW,~~a  C $4$4444 oinnUVFWFW o o]`al]m]m o o544 >>!   ! ! i q(9(9::O$&HR$:$: ! "RX 6 6 )+ &.0 +%*/!  ^^A  ! # # hr2844O(1}11f1==AACCIIKKO$/$@ ! +_,< = )+ &.0 +%*/!   ?233 9?233#7 7 7wyrss+,,rwyrss7K/L/LLLM''**"-#"'  '!**  M''**"-#"'  '!** %q=$q=(003399;;=M=U=UVW=X=X=^=^=`=`aa((**""$$  , H < < < < < <g+K44[AA GWXinnQ&7&7tDDDNGW-- - -11a4=$555(,N1% -&)::??Q?GGN*..6.::>>@@FFHH>/N?KK +,< ='(89^ ^ + N26']":CCC D D  *Z003&("*,'!#7!;!;!;)T-BBzkQ&q111T5II#&rz2B_2T'U'UVW'X#Y#Y y~~a(()) @ @A,7111 8V,W &q ),,,()?)BCHHJJ >?/ ;  ! & +    r8rddict[int, np.ndarray]rerfrgrc h|d|d}|d} |d} |d} t} |s ||jz n ||jz} |D]}|vr||<|| vsJ|j| |<t}|D]}|||D]#}t |j| |dz| |<$|D]>}|||t|j | |dz | |<?|j r&|D]#}t|j | |dz | |<$| D]\}}|| vs|| vrt||j krQ|| kr| p || ko|}|r2| |tjd|d|d || |d kr8|| vr4|| vr0tjd|d|d | || D]m\}}t|d kr|st |fd nt|fd}|D]'}||kr||f}| ||(n| D]z\\}}}|| vs|| vr|| kr|r|| krO|rMt||jkr5| |tjd|d|d|d|{| | | |fS)zEHandle hotstart heuristics to remove unmatched or duplicated objects.obj_first_frame_idxunmatched_frame_indstrk_keep_aliveoverlap_pair_to_frame_indsrrzRemoving object rOz# since it is unmatched for frames: rz, due to being unmatchedr<c|SrirXr[rs r7z>SAM3VideoSemanticPredictor._process_hotstart..ps 7J17Mr8)keyc|SrirXrs r7rz>SAM3VideoSemanticPredictor._process_hotstart..rs?!"23%-.J%K""#45 #?Fk D$777IX\XkLk & > >F000.7#F+////%)%=N6 " "uu $>$E$E$G$G 6 6    4 5 5 5 5" ^ ^F%()@.QWBX[\B\%]%]N6 " "+ ^ ^F  ( / / : : :&))@.QWBX[\B\%]%]N6 " "  : b+ b b),T-DnU[F\_`F`)a)av&&&:%?%?%A%A 2 2 !FM((F6K,K,K=!!T%AAA&9&&AM&Q&aZaVa&'/-?KG#&)--f555LM6MMYMM=JMM v&!++/11"777 eee)eeefff%))&111'A&F&F&H&H F F "A"&''!++ T'-M-M-M-MNNNN,2R2R2R2RSSS . F F000.7C.s3::9EEE F6P5U5U5W5W   1 "\6M((F6K,K,K#F+m;;G;#F+m;;;}%%)AAA)--f555Lj6jjYjj@LjjZgjj 4555$h..r8rrdc,t|dkrdStj|d|jdd}t |dr|jr|jj |}tj |dk d d d }d}|}|D]}t|d } | dkr|| z} ||| } ||| } | d} |j| | | d| }|\}}|d}dD]c}|||vr ||||d<d|D|||d<|j||||||d|| z }dS)zHRun Sam2 memory encoder, enforcing non-overlapping constraints globally.rNrrFrrcrrrrr)rrnrxrrcg|]}|SrXrX)rZposs r7r]zGSAM3VideoSemanticPredictor._tracker_update_memories..sIoIoIoRU#IoIoIor8r)rnr{rr)r`rrr+rrorcrrJ"_suppress_object_pw_area_shrinkager>rrrrr)r3rr{rdrr start_idx_gpustart_idx_state tracker_statenum_obj_per_state end_idx_statelocal_high_res_maskslocal_object_score_logitslocal_batch_size encoded_memlocal_maskmem_featureslocal_maskmem_pos_encrxrs r7rtz3SAM3VideoSemanticPredictor._tracker_update_memoriess( ' ( (A - - F  # #A & &#    t011 cT5K c!\/RRSabbN#k>A+=*B*Bx*B*P*PRVX]^^ '5" 1" 1M #M)$< = =  A%%+.??M#1/-2O#P (;OM4 8 8 8`d .);=#5#5a#8#88888..00000  # #A & &#    '!**  &) %( ]$C$C   J L ( ( 1#!z* )     112CDDD##$5666##r8c|sdSg}|D]R}|D]}|j||d t|ddkr||S||dd<dS)zgRemove an object from SAM2 inference states. This would remove the object from all frames in the video.NFrrr)rrr`r)r3rr active_statesstaterzs r7rz2SAM3VideoSemanticPredictor._tracker_remove_objectss  F ) , ,E! H H **5&*GGGG5#$$q(($$U+++#0QQQr8ctjgtjtjdtjdiit t id}it t t tt t td}|j rBtjgtj tjgtj d|d<||d<|S)z%Initialize metadata for the masklets.rr:)rrr]rrrr^)rrrrr)rconsecutive_det_numrr) r@rrrrr)rrJrrr)r3rrs r7r z/SAM3VideoSemanticPredictor._initialize_metadatasxBH--x28,,!2=d2C2C')  $&$/$5$5)#..*5d*;*;"uu     + (2rx00(*xBH'='= 00H+ ,(0$r8rjrkc|d}|d}|d}|j|jksJd|jd|jdt|Dtj||} || } tjfd| Dtj} |j} tj|| } || | | <tj|}|| || <tj||}| D]}|tj||z}tj ||d zd }||j k}|j | |<| |d<||d<|S) zZUpdate the confirmation status of masklets based on the current frame's detection results.rrrzGot rci|]\}}|| SrXrX)rZrrzs r7rzQSAM3VideoSemanticPredictor.update_masklet_confirmation_status..9s c c cf c c cr8c g|] }| SrXrX)rZrzobj_id_to_updated_idxs r7r]zQSAM3VideoSemanticPredictor.update_masklet_confirmation_status..=s V V Vv "6 * V V Vr8r)rrr) rwrr@rvrrr full_like zeros_likerrr CONFIRMED)r3rrjrkrdreconfirmation_data status_prevconsecutive_det_num_prevprev_elem_is_in_updatedprev_elem_obj_ids_in_updatedprev_elem_inds_in_updatedunconfirmed_valrr is_matchedrchange_to_confirmedrs @r7rwz=SAM3VideoSemanticPredictor.update_masklet_confirmation_status's%%;<(1 #45J#K  $8$>>>> F;$ F F*>*D F F?>>!d c Ja@b@b c c c"$'*>@W"X"X';loadload_state_dictr`r rminfo)r3r"rsd missing_keysunexpected_keyss r7_load_checkpointz+SAM3VideoSemanticPredictor._load_checkpointWs Z D I I I' R(,(<(s@r7rr s CCOLNOKIN F "&#&! !AD37%*',$&$)56'*&)WVCVCVCVCVCVCVCp======^^^^^44\40FFFFF333j4444l %%%%N4&*i i i i i V   \        .999"7777,($($($($TR9R9R9R9vD,D,D,D,D,L"$"$"$"$H -1 $$$$\$V!% BBBBBHDDDD8C C C C Jm/m/m/m/^81818181t*$*$*$*$X0000$" " " H....`WWWWW333  \     r8r)6r; __future__r collectionsrrrrtypingrrr)r@r>torch.nn.functionalnn functionalrultralytics.data.augmentrultralytics.engine.predictorr ultralytics.engine.resultsr ultralytics.utilsr r r ultralytics.utils.metricsrrultralytics.utils.torch_utilsrramgrrrrrrrrrsam3.geometry_encodersrrr@rkr.rfrwrrrXr8r7rAsL#"""""00000000  ......666666......666666666677777777MMMMMMMM                      +*****@ '@ '@ '@ '@ ' @ '@ '@ 'FKCKCKCKCKCIKCKCKC\AGAGAGAGAGAGAGAGH I I I I I mI I I X RRRRRMRRR0I I I I I MI I I X3/3/3/3/3/+]3/3/3/lJJJJJ!6JJJJJr8